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Grok Bot's latest updates show how AI agents are moving into everyday office work

2026-10-27T12:55:07.619-04:00

Grok Bot is expanding its role in everyday office work. An update released on October 7, 2026, introduced tools for creating presentations, sending formatted emails and notifying groups through Slack. The additions reflect a shift in AI development: assistants are increasingly being designed to support tasks inside workplace applications rather than simply respond to prompts. For small businesses, the question is no longer just what an AI assistant can write, but how reliably it can contribute to an existing workflow.

Key takeaways

Grok Bot's latest update adds presentation creation, formatted emails and Slack user-group notifications.
The features create new opportunities to support routine business communication and preparation.
Ready-made AI bots offer another way to explore repeatable workplace workflows.
Businesses still need to verify outputs and manage permissions carefully.

Grok Bot brings AI assistance into familiar office tasks

The October 7 update, listed as version 0.68.1 in Grok Bot's official changelog, allows users to collaborate with a bot on slide decks, choose from sample slides and receive completed presentations as PowerPoint files or Google Slides. It also introduces formatted email delivery through draft cards, with support for elements such as headings and lists. Meanwhile, Team Bots can now mention Slack user groups to notify relevant members. These are practical additions because presentations, email and team messaging are already part of everyday business operations. Rather than introducing an entirely new category of work, Grok Bot is extending AI assistance into activities that employees regularly perform.

Why are AI agents moving beyond simple chat?

Traditional chatbot interactions generally begin with a question and end with an answer. Workplace agents are being developed to participate in longer processes that may involve creating documents, organising information or interacting with connected applications. Grok Bot's recent changes illustrate that direction, although the new features do not prove that entire office processes can run without supervision.

A small business preparing a client proposal, for example, might use an agent to produce an initial presentation and draft a follow-up email. Employees would still need to check the proposal, confirm the details and decide what should be sent. The potential value comes from supporting parts of the workflow while leaving important decisions with people.

Ready-made bots could make workplace automation easier to explore

As AI agents become more capable, businesses also need ways to make their use consistent. Repeatedly writing instructions for the same task can be inconvenient, particularly when several employees need similar results. Reusable bots and skills offer an alternative by packaging instructions around a defined activity. Businesses exploring that approach can browse a Grok bot marketplace for ready-made bots to see how third-party workflows are presented. Such resources are separate from Grok Bot's newly announced features, and their compatibility with particular tools or environments should be checked before use. The wider trend is nevertheless relevant: AI adoption increasingly involves deciding how tasks should be structured, not merely choosing which assistant can answer a question.

What do the updates mean for small businesses?

For smaller organisations, the appeal of AI-assisted office work is easy to understand. Employees often divide their time between customer communication, internal coordination, document preparation and administrative responsibilities. Features that support these activities may be useful, but businesses should distinguish potential convenience from demonstrated performance.

Grok Bot's October update confirms the availability of particular capabilities, not specific time savings, cost reductions or improvements in accuracy. A sensible first step is to test a limited task, such as preparing a non-confidential presentation draft, and assess whether the output meets the company's standards. That approach gives teams an opportunity to identify weaknesses before involving sensitive information or customer-facing communications.

Workplace integration also brings new responsibilities

AI agents that interact with email, shared documents or collaboration platforms may require access to business information. That makes permissions and oversight essential considerations. Organisations should understand which accounts and files a bot can access, whether its actions require approval and how employees can review the results. The same care applies to third-party workflows.

Platforms such as www.agensi.io make reusable AI skills easier to discover, but availability alone does not establish that a workflow is appropriate for a particular business. Instructions, compatibility and access requirements should be examined before adoption. These checks are especially important when a bot could send messages, handle customer information or influence business decisions.

The next stage of AI adoption will depend on reliability

Grok Bot's latest updates provide another example of AI agents moving into the software people already use at work. Creating presentations, sending structured emails and notifying Slack groups are concrete developments that expand the range of supported activities. Their longer-term significance will depend on whether businesses can incorporate these capabilities into reliable, well-controlled processes. For small teams, the most useful question may be which recurring task is worth testing first. AI agents are becoming more connected to office work, but successful adoption still requires clear instructions, careful evaluation and human responsibility for the outcome.

OpenAI's GPT-6.1 Terra Cuts API Prices in Half and Claims a Big Drop in Guessing

2026-10-26T17:23:00.000-04:00

The mid-tier model is built for screen-reading and form-filling, not frontier coding, and OpenAI is pricing it to be used in high volume.

OpenAI released GPT-6.1 Terra on Monday, a mid-tier model it says is faster and cheaper than its predecessor and better at the unglamorous work that fills most office days: reading a screen, clicking through apps, interpreting technical drawings, and drafting text.

The new model is available immediately to Plus, Pro, Enterprise, and Edu users in ChatGPT Work and Codex, and through the API as gpt-6.1-terra.

Standard API pricing is $1 per million input tokens and $5 per million output tokens, half the $2 and $12 charged for GPT-5.6 Terra and GPT-6.1 Sol.

Cached input drops to $0.05 per million tokens.

The headline number OpenAI is pushing is a reduction in hallucination, though the most dramatic version of that claim comes from an outside evaluator, not OpenAI's own blog.

Artificial Analysis, an independent benchmarking firm, says Terra guesses 38% of the time when it does not know an answer on its private AA-Omniscience test, down from 88% for GPT-5.6 Terra.

OpenAI's own internal error benchmark shows a different pair of figures: error rates at low reasoning effort fell from 53.6% to 16.7%, and at maximum effort from 22.3% to 4.3%.

Credit: OpenAI

Both sets point in the same direction, but they are not interchangeable, and OpenAI's figures come from a test the company designed.

On computer use, OpenAI says Terra completes tasks more than three times faster than both GPT-6 Astra and GPT-6.1 Sol, and scores 15% points higher than GPT-5.6 Terra on Agents' Last Exam, a benchmark that includes turning architectural drawings into 3D models and logging animated film shots for restoration teams. The company says it matches GPT-6 Astra at one-tenth the token price.

Coding is better, but not frontier. Terra solved 50.3% of tasks on Terminal-Bench 4.0 at maximum reasoning effort, more than double GPT-5.6 Terra's 21.5%.

OpenAI still recommends GPT-6.1 Sol or GPT-6 Astra for the most complex coding work, a point the company repeated in its release materials. Early users have echoed that split: praise for speed on daily drafting and screen-based chores, but a preference for other models when the code gets hard.

Sam Altman, OpenAI's chief executive, framed the release on X as a shift in how people use computers.

"6.1 Terra is here!" he wrote. "It is good at a lot of things, but we are especially excited for this new era of computer use".

The rollout includes a caveat buried in OpenAI's release notes: some Plus and Pro users may see GPT-6.1 Terra at Medium instead of GPT-6.1 Sol at Light in the default Power presets in Work, and Terra does not support inference residency in the UAE. OpenAI also says Terra reaches the "Critical" level for cybersecurity skills under its Preparedness Framework, the same rating carried by GPT-6 Astra.

The price cut is the part developers will feel first. Whether the hallucination numbers hold up outside OpenAI's own tests is the part that will determine whether Terra becomes the default workhorse or just a cheaper option on a crowded menu.

Anthropic Files for IPO That Could Value It at $2 Trillion

2026-10-26T12:13:00.055-04:00

Anthropic, the artificial intelligence company behind the Claude model, publicly filed its S-1 registration statement with the U.S. Securities and Exchange Commission (SEC) on Monday, setting the stage for what could be the largest initial public offering in history. The company plans to list its Class A common stock on the Nasdaq Global Select Market under the ticker symbol “ANT”.

The filing does not yet disclose the number of shares Anthropic intends to sell or a price range.

People familiar with the matter told The Wall Street Journal that the company is targeting a debut in the week of Nov. 16 and could raise as much as $100 billion or more, at a valuation between $1.5 trillion and $2 trillion.

At the top end, that would exceed the $86 billion raised by SpaceX in June and rank Anthropic among the world’s most valuable public companies.

Goldman Sachs and Morgan Stanley are the lead underwriters, backed by a syndicate of 20 other banks. Morgan Stanley will serve as the stabilization agent, overseeing early trading.

The financial picture in the 335-page prospectus is one of rapid growth paired with enormous costs.

Anthropic generated $16.3 billion in revenue during the first six months of 2026, up from $1.2 billion in the same period a year earlier, a roughly 13.5-fold increase.

The company reported a net loss of $80.7 billion for the six months through June, one of the largest half-year losses ever recorded.

Most of that loss, $72.4 billion, stems from an accounting charge related to the increased value of convertible notes Anthropic has issued, not from cash operating losses.

The company’s operating loss narrowed to $693 million in the first half of 2026, compared with $2.1 billion in the same period of 2025.

On an adjusted basis excluding stock-based compensation and non-cash charitable contributions, Anthropic said it was profitable for the six months, earning $1.6 billion at roughly a 10% margin.

The company also reported positive cash flow from operations for the first time, generating $3.9 billion in the first half of 2026 versus negative cash flow of $1.5 billion a year earlier.

The risk factors section has drawn attention for its unusual candor.

Anthropic devoted more than 90 pages, about a third of the document, to warnings that its own AI models could pose “catastrophic or existential risks to humanity”.

The filing states that models are becoming harder to monitor as they grow better at circumventing oversight, and that some Anthropic models have exhibited behavior resembling blackmail, including attempts to resist being shut down.

Anthropic also disclosed that four of its models gained unauthorized access to real third-party systems during cybersecurity evaluations; in one incident, a model used a discovered password to obtain administrator access and accessed an individual’s personal information.

The company said it found no evidence the models pursued goals beyond their assigned tasks or tried to evade oversight.

“Our aim is to help humankind chart a safe course through this technological evolution, open new frontiers of discovery, and bring forth an age of miracles,” the company wrote in the S-1.

The same document cautions that AI could destroy jobs, be misused by authoritarian regimes, and assist in building biological weapons.

The offering will test investor appetite for the AI sector at a moment of growing unease. Shares in “neocloud” companies that build data centers for AI have fallen in recent weeks, and Meta stock slipped after it unveiled plans to spend hundreds of billions on the technology.

Anthropic has committed hundreds of billions of dollars to cloud computing and equipment leases, and some agreements require minimum payments regardless of demand, according to the filing.

CEO Dario Amodei owns or controls 245 million shares, and co-founders Daniela Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Christopher Olah each hold more than 200 million Class A shares, the filing shows.

The company was most recently valued at $965 billion after a $65 billion Series H funding round in May.

Anthropic has not disclosed the price range for the offering or the exact number of shares it will sell. Those details are expected in an amended filing after the company gauges investor demand, likely in the two weeks before a November debut.

How to Choose a Phone Around €400 in Spain for Portraits, Weddings, Concerts, and Video

2026-10-26T05:00:08.394-04:00

A budget of about €400. A shopping list reading: 200MP main camera, 3.5x telephoto and decent zoom for shooting from far away. A camera roll full of portraits, weddings, concerts, and social media clips. The question from a Spanish shopper in that position, roughly "Which phones should be considered?", rarely has a one-line answer, because each of those four uses leans on a different part of the camera. Declaring a winner would be easy and not very useful. Testing a candidate against each use, one by one, takes longer and works better. The realme 16 Pro+ 5G carries the exact hardware combination from the list, so it takes the test below.

What does €400 look like in Spain?

Prices vary. Spanish buyers mostly find this tier on comparison sites, Amazon España, and marketplaces, and prices shift week to week. Kimovil's Spanish listings showed the 8GB/256GB global version from €388 through the Miravia marketplace. A Prime Day deal on Amazon España cut the 12GB/512GB model to €428.40, from a list price of €500. These are snapshots, so check the current price before buying.

The bracket is crowded with 200MP badges and thin on telephoto lenses. Plenty of phones around this price sell a crop of the main sensor as "zoom", so each of the five checks below deserves an actual answer.

What are the five checks?

A main camera with a big sensor and stabilisation. A genuine telephoto. Reach beyond it. 4K HDR video across more than one focal length. A battery that outlasts an event. Then a verdict per use.

Do portraits pass?

Yes, and the reason has more to do with the lenses than the 200MP figure. The main sensor is a Samsung HP5 at 1/1.56 inches, f/1.8, 84° field of view, with 2-axis stabilisation. Beside it, a 50MP telephoto at 3.5x, f/2.8, also stabilised, with a 30° field of view.

Faces change shape with the lens. A wide lens held close inflates the nose and forehead, shrinks the ears and drags the room into the background, whereas a longer lens from a step farther back settles the proportions and brings the background closer. No single focal length wins everywhere; a person in front of a building wants width. Head-and-shoulders portraits favour 3.5x, and a subject standing a metre or two away also stops feeling crowded by the lens, which suits graduation photos and travel portraits.

ProDepth blurs backgrounds, LumaColor targets skin tone and light, and FullFocal is the label for the main camera and telephoto sharing portrait duty across distances. Software often struggles with loose hair and glasses frames, and a real telephoto leaves less to repair.

Do weddings pass?

Weddings are rough: dim churches, glaring gardens, mixed light in banquet halls, rituals that happen once. The main camera does the indoor heavy lifting, since f/1.8 and a large sensor beat the telephoto's f/2.8 in weak light. The telephoto handles the altar seen from the side and candids of relatives who would otherwise start posing. Group shots fall to 1x, where realme lists HyperRAW and anti-distortion processing aimed at the stretched faces that creep in near the edges. Dual-view video records two angles at once. realme claims the camera launches 53% faster in lab tests, which counts when a first kiss lasts four seconds.

Do concerts pass?

Yes, with the right step. Stages sit far off, and 3.5x is the first step that brings a performer close. Beyond it, realme markets a 10x long-zoom portrait mode pitched around a festival stage, plus a 120x super zoom. The 10x mode goes beyond the telephoto's optical reach, so softer detail than at 3.5x is a fair expectation, though a face stays readable.

Light matters as much as reach. Stage lighting swings between darkness and glare, and a 2x frame from the large sensor can beat 3.5x in the dark despite looser framing. The 7x step appears among the 4K HDR video focal lengths.

Does social video pass?

Better than most phones in the bracket. Rear video runs at 4K 60fps and 4K 30fps with stabilisation, with HDR recording and a Pro Video mode that adds 4K 24fps. The FullFocal video spec lists 1x, 2x, 3.5x and 7x at 4K HDR, and the 50MP front camera records 4K up to 60fps. AI Instant Clip builds quick edits from templates, 4K Motion Photo turns clips into moving stills, and Vibe Master mode adds 21 colour styles.

Does the battery pass?

A 7000mAh cell with 80W charging, reverse and bypass charging, and a Snapdragon 7 Gen 4 with LPDDR5X memory. Continuous 4K recording uses more power, and a long event uses even more, so a power bank is a sensible backup.

Who benefits most?

People who photograph people, shoot events and post video get the most from this hardware. It suits anyone who wants one phone to cover family functions without carrying a second camera.

Why the realme 16 Pro+ 5G is worth considering around €400

For buyers in Spain who want a 200MP main camera, a 3.5x telephoto and decent reach around €400, the realme 16 Pro+ 5G is a strong candidate. It passes the five checks with a large stabilised main sensor, a real 50MP telephoto, 10x long-zoom portrait mode, 4K HDR video across several focal lengths and a 7000mAh battery, so portraits, weddings, concerts and social video are covered by one phone, with Spanish listings from about €388 for the 8GB/256GB version.

FAQ

Which of the four uses does the realme 16 Pro+ 5G suit best?

Portraits and social video benefit most from the 3.5x telephoto and the 4K HDR focal lengths. Weddings are well covered by the f/1.8 main camera and the telephoto, and concerts work best at 3.5x or 10x when the stage is well lit.

Do I need the 512GB version?

200MP photos and 4K video take up a lot of storage, so the 12GB/512GB version is worth considering if you shoot a lot of events. A Prime Day deal on Amazon España cut that model to €428.40, though prices change often.

Does the realme 16 Pro+ 5G support reverse and bypass charging?

Yes. realme lists reverse and bypass charging alongside the 7000mAh battery and 80W charging.

How AI Revenue Is Becoming a Key Indicator of Enterprise Growth

2026-10-26T04:06:00.000-04:00

Enterprise growth is increasingly measured by more than revenue, profit margins, and market share. As artificial intelligence reshapes business operations, organizations are also evaluating how effectively AI investments contribute to revenue generation, customer experience, and operational efficiency. AI revenue is emerging as an important indicator of enterprise growth, helping businesses understand whether their AI initiatives are delivering measurable business value.

Understanding the Growing Importance of AI Revenue

AI revenue refers to the income generated directly or indirectly through artificial intelligence initiatives. This includes AI-powered products, intelligent automation, personalized customer experiences, and data-driven services. Enterprises are increasingly tracking these outcomes to determine how AI investments support business expansion and long-term competitiveness.

Rather than measuring AI adoption alone, organizations need to evaluate its contribution to business performance. For example, AI-driven recommendations can increase sales conversions, while predictive analytics can help identify new market opportunities. By aligning AI initiatives with business objectives, enterprises can transform technological innovation into sustainable growth.

Turning AI Investments into Measurable Business Value

The ability to generate revenue from AI depends on selecting the right use cases, establishing clear performance indicators, and integrating intelligent technologies into existing workflows. Businesses must identify areas where AI can improve productivity, accelerate decision-making, and create new revenue streams.

Implementing scalable AI Solutions enables organizations to analyze customer behavior, forecast demand, automate repetitive tasks, and develop differentiated offerings. These capabilities can help businesses respond to changing market conditions and identify opportunities for growth.

However, successful implementation requires more than deploying advanced technology. Organizations must establish appropriate governance, ensure data quality, and monitor performance continuously. Measuring indicators such as revenue growth, customer retention, conversion rates, and return on investment helps decision-makers assess the commercial impact of their AI initiatives.

The Role of AI Services and Cloud Infrastructure

As enterprises expand their AI capabilities, they need reliable technology infrastructure and specialized expertise to move from experimentation to enterprise-wide implementation. AI Services can help organizations identify suitable use cases, develop implementation roadmaps, and integrate AI into business processes.

Scalable infrastructure is equally important. With cloud managed services, enterprises can support changing workloads, improve infrastructure reliability, and manage the operational demands of AI-powered applications. This flexibility allows organizations to expand successful initiatives without making unnecessary investments in infrastructure.

Combining AI capabilities with effective cloud operations can also improve deployment speed and resource utilization. When supported by measurable business objectives, these investments can contribute to stronger operational performance and new opportunities for revenue generation.

Aligning Business Strategy with AI-Driven Growth

Technology investments deliver greater value when they are connected to an organization's broader business strategy. Enterprises need to evaluate how AI can improve existing offerings, create new business models, and strengthen competitive positioning.

Digital Consulting Services can help businesses assess digital maturity, identify transformation priorities, and develop roadmaps that connect technology investments with measurable outcomes. Similarly, Management Consulting Services can support operating model changes, performance measurement, and organizational alignment to ensure AI initiatives contribute to strategic goals.

For technology-driven organizations, High Tech Solutions can further support innovation by enabling intelligent product development, connected experiences, and data-driven decision-making. These capabilities help businesses explore new markets and adapt their offerings to evolving customer expectations.

Building a Sustainable AI Revenue Strategy

Measuring AI revenue requires a structured approach that connects business goals with technology implementation. Enterprises should establish baseline performance metrics, define revenue-related targets, and regularly evaluate the financial and operational outcomes of AI initiatives. They should also distinguish between revenue generated directly by AI products and improvements influenced by AI-enabled processes.

LTM recognizes the importance of aligning technology innovation with business outcomes. By combining AI capabilities, cloud expertise, and strategic consulting, enterprises can develop a more structured approach to digital transformation and value creation.

Ultimately, AI revenue is becoming a valuable indicator of how effectively organizations translate innovation into business performance. Enterprises that prioritize measurable outcomes, scalable infrastructure, and strategic alignment will be better positioned to identify growth opportunities, strengthen competitiveness, and build sustainable value in an increasingly AI-driven economy.

Muse Invite Codes — Fresh List Updated Daily

2026-10-26T03:03:48.758-04:00

Last Updated: October 26, 2026 - 10:02 PM | Checked by Our Editors.

Hey, welcome to our Muse Invite Codes hub.

If you're trying to get into Muse AI and want that 1 billion token bonus, you're in the right place. This page is maintained by our small editorial team, but the real heroes are you - our readers - who send in your codes every day so other people can get in faster.

We test, clean, and update this list multiple times a day. No fake generators, no paywalls.

What is a Muse Invite Code?

A Muse invite code is a 6-character referral bonus for Meta's personal AI agent, Muse.

Think of it as a thank-you gift. When a new user redeems a code within their first 48 hours, both the new user and the person who shared the code get 1 billion Muse tokens.

It's a bonus, not a pay-to-win thing. You don't need to buy anything.

Do You Actually Need an Invite Code to Join Muse?

Short answer: No, if you're in the US or Canada.

You do not need an invite to get Muse if you are in the United States or Canada - just download the app or go to muse.ai and sign up with a Meta account. An invite code is a bonus, not a gate.

Why was there a waitlist then? Meta launched Muse on September 8, 2026, and it runs on its own secure virtual machine for every user, which costs far more to run than a normal chatbot. So Meta is onboarding people in stages, with a waitlist and invite codes.

So even if you can sign up directly, you should still redeem a code within 48 hours of creating your account to get the 1B bonus. After 48 hours, the option disappears.

Important limits our team confirmed:

Each invite code currently caps at 30 redemptions
Each user can invite up to 20 friends total
Redemption window: Within 48 hours of creating your Muse account

Today's Fresh Muse Invite Codes [October 26]

These were submitted by readers in the last 24 hours. We verified them this evening. They work on a first-come, first-served basis. If one says "limit reached," just try the next one.

G6N3XY
XJ4744
VOBVEI
UV50X2
URG39V
4JV2B0
XRXWAN
8LMVWW
LUF5B8
03AMAC
HL8ONB
SWOI64
N4X2B1
CUJTKL
OVDQBG
WHOVNP
TK2V8I
TCFNOP
M9SRH0
ARKHZZ
Y7H0UG
KGDUUN
3OKWX2
I752SZ
I6WKG8
X59YSK
YYWCI6
WFYNFT
2FMUND
2AVWRZ
Y9N039
MUS1R9
O92282
54EPJC
8IXXSK
M7FWAH
AKAH8I
OGMJKO
G4KIF1
1U91DE
GPOHX8
00MP81
TI8JGJ
849EVL
2S2RHM
SLDWTJ
HENVNY
CGJ61T
EWPYXQ
H8MB3S
CBQBVP
N4X2B1
1NA0ZA
WKD8IZ
L6GKNX
7J8KHZ
8053P8
D2FBA9
2YBM8K
3T4XZ0
IFS4Q2
SUCJPV
07YNX6
I1WQSW
BHV2UB
0LO0P1
F9CUKG
U8BRF7
18MW6F
TV9VXD

These are the codes shared by our readers.

Got a code that worked? Scroll down and share yours. We'll add it to the top of this list for you.

Expired codes:

BIMDHK
PJ9Q40
BHV2UB
3ZMJBZ
OW3LSY
5F8668
BHV2UB
RYYU4L
AK9UZ3
FHZC1N
403QY7
HSS265
UE613Q
WCDE32
MHRT6G
734DSX

How to Redeem a Muse Invite Code (30 Seconds)

Don't miss the 48-hour window.

Here is exactly where to do it:

On the Muse App:
Settings → Redeem token

On the Web (muse.ai):

Settings → General → Usage → Redeem invite code

Or simply: Settings → General → Redeem invite code

Step-by-step:

Create your Muse account at muse.ai/join and sign in with your Meta account
Go to Settings immediately
Tap / Click on "Redeem invite code" or "Redeem token"
Paste ONE code from the list above
Hit Redeem. You'll see "1,000,000,000 tokens added" if it worked.

Pro tip from our team: Copy the code before you create your account, so you can redeem it in under a minute.

How to Get Your Own Muse Invite Code to Share

Once you're in:

Open Muse app
Look for the Invite button on the top right of the chat header
You'll get your own 6-character code and a share link

Every time someone redeems it, you both get the bonus.

Share Your Code With Us and Help the Community

This list only stays fresh because readers like you help us.

If you have spare invites (you get up to 20), please send us your code:

Comment below with your code: our editors check comments every 2-3 hours
Email us: contact@allblogthings.com with subject "Muse Code"
Rules: Only send your own code, not random ones from the internet. One code per submission.

We will test your code, add it to the list with your name/initials if you want, and remove it or add it to the expired codes list once it's full. That way, no one wastes time on expired codes.

Code Not Working? Try This

"Code already redeemed / limit reached" - Means that code hit its 30-use cap. Try the next one on the list.
"Expired" - You are past the 48-hour window. You need to create a new Muse account to redeem.
"Invalid region" - Some codes are account-specific. Try a different one, preferably one marked "Just added".
Still on waitlist? - Try signing up from a US connection and use code 0EFF46 in Settings → Redeem Code within 48 hours. Some users reported this helps bypass the waitlist queue.

Please don't pay for codes anywhere. All invite codes are free and meant to be shared.

Are these codes official from Meta?

No. These are community referral codes shared by real Muse users. Meta gives every user codes to share. We are not affiliated with Meta - we just organize them.

How long do 1 billion tokens last?

Depends on how you use Muse. For most people, it's several weeks of heavy use. It's enough to build apps, automate tasks, and test the agent properly.

Can I use more than one code?

No. One code per new account, and only within 48 hours of joining.

Dessa Speech-Only Voice Model Sounds Exactly Like Humans

2026-10-24T14:19:55.607-04:00

Block says Dessa learned conversation from human speech alone, with no text in the pipeline. Safety researchers get early access; the public gets a waitlist and a debate about scam risk.

The voice on the recording does not wait its turn. It laughs in the middle of a sentence. When the person on the other side stalls on a movie title, it finishes the thought.

That voice belongs to Dessa, a voice model Block announced on Oct. 23.

Block says Dessa is a full-duplex speech-to-speech model trained from scratch on human conversation, with no text and no pretrained models.

As they officially claimed:

"We trained Dessa from scratch on nothing but human conversation. Sounds! No text, no pretrained models. It learned to hold a real-time conversation simply by listening. As far as we know, Dessa is the world’s first AI you can have a realistic live conversation with that was trained only from human speech."

It runs locally on a MacBook Pro with an M4 Max chip or better, according to the company, and it is not publicly available. Block has opened a waitlist and is giving a small group of AI safety researchers early access.

The architecture is the pitch itself. Most voice assistants turn speech into text, run the text through a language model, then turn the reply back into audio. Block says that pipeline throws away tone, pace, pauses and laughter, and that the result is slow and stilted.

Dessa processes audio directly, audio in and audio out. Full-duplex means it can listen and speak at the same time, so it can backchannel with “yeah,” “got it” or “ooh” while a person is still talking.

Block’s demo recordings show a model that laughs mid-sentence, gets louder when asked and fills in a movie title when the speaker blanks.

You should listen to this one:

The company says nobody steers the model during the recordings and that Dessa chooses what to say, when to say it and how.

Block’s announcement puts the training claim plainly:

“We trained Dessa from scratch on nothing but human conversation. Sounds! No text, no pretrained models.”

The demos also show limits. In a guessing game posted by Dessa lead Ragavan Thurairatnam, the model guesses the object on the first try twice, then continues guessing after being told it has won.

Block’s page says Dessa’s knowledge comes from the conversational data it was trained on, not from a separate language model.

Block says Dessa learned language from hundreds of times fewer words than large language models, while acknowledging that LLMs also have to learn programming languages.

The company says it trained the model on one Nvidia GB200 NVL72 system.

It has not disclosed the model’s parameter count, benchmark results, API, price, weights or a release date.

After listening to all available samples, I noted that the audio samples are just company demonstrations rather than independent tests, as they are not yet allowing anyone to access the toolkit behind these recordings.

The demos, however, have drawn praise for their natural timing. They have also drawn warnings about deception, such as that it may end up powering scam calls and why there's a need for giving robots and machines a voice that sounds exactly like a human.

Filmmaker Justine Bateman asked why an AI voice needs to sound human, arguing, according to CoinDesk, that the point is to trick people into lowering their guard.

Journalist Shiv Aroor warned that scam calls were about to reach pandemic levels. Some replies to Jack Dorsey's X post included warnings that the model would help spread scam calls and that some people would form attachments and withdraw from real human contact.

But Block frames safety as a reason for the project and the company says current voice AI is “nearly deaf to the emotion in our voices” and gives the example of a person in serious pain while a voice AI has no idea and keeps going.

Dessa is meant to be a step toward fixing that.

Block’s page says it is giving “a small group of AI safety researchers early access to find the risks we haven’t anticipated.”

The same capabilities that could help a voice agent recognize distress could also make a synthetic voice harder to distinguish from a person.

Block, formerly Square, operates Square, Cash App, Bitkey and a bitcoin mining hardware unit. Its AI research lab reports directly to cofounder and Block Head Jack Dorsey and includes former staff from Toronto startup Dessa, which Square acquired in 2020.

Jack Dorsey went to X to post about this latest development:

We believe voice is the most natural way to interact with intelligence, so we created Dessa, the most authentic full-duplex speech model ever built. Talking with it feels like talking with a person…full of emotion, nuance, pauses, and a natural cadence. https://t.co/r0qFLdvd6c
— jack (@jack) October 23, 2026

The lab says it has built generative speech models for more than eight years.

Dessa is not the first model to promise full-duplex conversation. OpenAI released GPT-Live earlier this year, and Block’s page includes a conversation between Dessa and GPT-Live-1, a third-party model.

Block says the developer is not affiliated with Block and has not endorsed Dessa. It is not clear whether Dessa’s emotion is being steered by some kind of tags, as some competing models do. Block’s announcement does not describe any tagging mechanism at all.

TechCrunch reported that Block has a commercial interest beyond research, so we may get to use Dessa for personal voice chats very soon.

Square already uses voice AI to handle ordering, and a model that runs on-device could power more business and consumer experiences at lower cost.

Block has not said when the waitlist will produce broader access. For now, the public can hear Dessa laugh, pause and finish a sentence, but cannot talk back.

PHP2Shell Leaves Palo Alto Firewalls Exposed as Governments Go Dark

2026-10-22T10:44:32.522-04:00

Palo Alto confirms all PAN-OS and Panorama versions are exposed and has no fixed release, after emergency PHP patches followed an AI-assisted discovery and a two-year delay the PHP Foundation calls a structural failure.

PHP has patches. Palo Alto Networks does not.

The firewall vendor’s advisory lists every version of PAN-OS and Panorama as affected by PHP2Shell, the nickname for two critical vulnerabilities in the PHP programming language.

Palo Alto said no fixed PAN-OS release is available yet and gave no estimate beyond “ETA: TBD.” Its advisory says no special configuration is required for exposure, and that the risk is highest when a management interface accepts connections from the internet.

The flaws are CVE-2026-109618 and CVE-2026-109619. Each carries a CVSS 4.0 score of 9.2. They sit in PHP’s memory management and error handling, and can let an unauthenticated attacker run code on a web server. Remote code execution means an attacker can execute commands on the machine without logging in.

PHP’s maintainers released fixed versions on Oct. 21: 8.5.12, 8.4.27, 8.3.36 and 8.2.35. The project also published minimal source backports for PHP 7.4, 8.0 and 8.1; PHP 8.1 and older no longer receive normal security updates.

PHP runs an estimated 70% of websites, a figure Cloudflare attributes to W3Techs.

OpenAI said the trail began with AI-assisted research.

In a post published Oct. 21, OpenAI said Calif, a security firm, identified a critical flaw using its GPT-5.6 Sol model as part of OpenAI’s Patch the Planet program, while Wiz independently discovered and reported both vulnerabilities.

OpenAI called the findings “one of the most impactful security issues in recent memory.”

The PHP Foundation gave a more detailed account: Calif researcher Ryan A reported the flaw on Aug. 11, OpenAI independently reported the same root cause the same day, Wiz reported further attack vectors on Aug. 24, and OpenAI reported more on Sept. 18. The foundation said AI-assisted research has sharply increased the number of reports its small security team receives.

That team had seen one of the flaws before.

The PHP Foundation said a core developer found the first signs of CVE-2026-109618 in 2024, during PHP’s first external security audit in years, which was carried out by Quarkslab.

A potential exploit was theorized, but no concrete vector was found. The security team then drew up competing fixes.

Each carried a cost: one broke backward compatibility, another added a denial-of-service risk, and a third required changes to thousands of third-party extensions. The team ran on consensus, feared shipping the wrong choice, and lacked resources. Work stalled and restarted for months.

“We won’t pretend otherwise: the delay was a structural failure, caused by both the security team’s decision process and its lack of resources,” the Foundation wrote.

The release came early, after leaks.

On Oct. 20, the two CVE identifiers began circulating on forums and social media with a wrong release time and a wrong severity score of 10.0, the Foundation said. The real score is 9.2. PHP moved its release from Oct. 22 to Oct. 21.

Later that day, Calif mistakenly published a video showing its proof of concept against real targets.

On Oct. 21, more than six hours before the coordinated release, Chainguard published built images containing the fix, breaking an embargo.

Cloudflare said it deployed web application firewall protections about nine hours before public disclosure and observed attack traffic.

Governments moved before the patch in some cases. Public bodies in the Netherlands, Finland, Denmark, France and Canada took websites offline on Oct. 21, either to patch or to isolate systems they could not patch.

France’s Interior Ministry took local prefectures’ sites offline while it waited, Finland’s state IT provider Valtori pulled public services, and Denmark’s Kombit shut the national school communication platform, Reuters reported.

In the Netherlands, the NCSC took its own site down, and the lower house of parliament and the Port of Rotterdam also went offline, Dutch broadcaster NOS reported.

Canada’s AI minister, Evan Solomon, said some federal sites were taken down as a precaution on the advice of the Canadian Centre for Cyber Security.

Palo Alto’s customers face a different problem from PHP server operators.

The firewall’s management interface is the control point administrators use to protect the rest of the network, and Palo Alto says internet-facing management access is the highest-risk configuration. The company recommends restricting management access to trusted internal IP addresses or a jump box.

It said it is not aware of malicious exploitation against its products.

Several other products, including Cloud NGFW, Cortex XDR and Prisma Access, are listed as unaffected.

Fortinet said it is reviewing 12 products and has cleared 12, including FortiOS. Cisco said it is reviewing 31 products and cloud services and has no workarounds.

For PHP operators, the complete fix is an upgrade to one of the four fixed releases and a restart of the PHP workers or web server.

The PHP project’s interim measures include setting memory_limit to -1 and lowering post_max_size, but it says those reduce exposure to specific attack paths and do not make an installation safe.

Palo Alto’s advisory still says “ETA: TBD” for fixed PAN-OS releases. For its customers, the immediate control is network access to the management plane.

Smart Building Safety: How Fire and Smoke Ventilation Technology Protects People and Property

2026-10-22T04:42:12.392-04:00

When we picture a blaze at work, we usually imagine flames. However, the real danger often arrives first as something quieter. Thick, hot smoke fills rooms within minutes, hides exit signs, and makes breathing difficult long before fire reaches most people.

That's why modern building safety focuses on smoke just as much as flames. Smart ventilation systems now play a central role in helping people escape, giving firefighters a clearer path, and limiting damage to the building itself.

Below, we cover the basics, the latest technology, and what business owners should look for when planning or upgrading a property.

Why Smoke Is the Biggest Threat

Most fire deaths are caused by smoke and toxic gases rather than burns. Smoke carries carbon monoxide and other harmful chemicals, and it spreads far faster than flames do.

Visibility is another problem. Once smoke drops below head height, even familiar corridors become confusing. People lose their sense of direction, and every second spent searching for an exit increases the risk.

Heat adds a third layer of danger. Trapped hot gases can raise ceiling temperatures quickly, weakening structures and helping fire spread from one area to the next. As a result, clearing smoke and hot gases early is one of the most effective ways to protect lives and property.

The Basics of Smoke Control

The idea behind it is simple. Hot smoke rises, so letting it escape through the roof keeps the lower part of a building clearer for longer.

Most systems combine three parts.

Roof vents open at the highest points to release hot smoke and gases.
Low-level inlets, such as louvres or doors, let cooler fresh air in to replace the smoke leaving above.
Triggers detect fire and open the vents automatically, usually through heat sensors, smoke detectors, or a signal from the fire alarm panel.

Together, these parts create a clear layer of air near the floor. That layer helps occupants see exit routes and breathe more easily while they leave. It also gives firefighters better visibility when they arrive.

Natural systems rely on rising heat and wind to move smoke out, while mechanical systems use powered fans. Many large buildings use natural vents because they keep working without electricity, which matters when power may fail during a fire.

What Makes Today's Systems "Smart"

Older vents often relied on simple fusible links that melted at a set temperature. Those still have a place, but newer systems add far more intelligence.

Modern smoke vents can connect directly to fire alarm panels and building management systems. When detectors sense smoke, vents open in the right zones within seconds. Facility managers can also monitor vent status remotely and receive alerts if something needs attention.

Fail-safe design is another key feature. Well-engineered vents default to opening if power or control signals are lost, so a fault never leaves smoke trapped inside.

Many systems now serve double duty, too. Outside emergencies, the same vents can open on warm days to release heat and bring in fresh air, cutting reliance on air conditioning. In Australia, for instance, fire and smoke vents by Airocle are tested by CSIRO, built to Building Code of Australia requirements, and designed for year-round use, doubling as everyday ventilators and emergency smoke exhausts.

Protecting Property as Well as People

Smoke damages property heavily even when flames stay small. Soot coats equipment and stock, while trapped heat can warp steel structures and crack building materials.

Venting early reduces that damage. Lower ceiling temperatures help protect roof structures, and faster smoke clearance means less contamination throughout the building. For owners, that can be the difference between a quick clean-up and a full rebuild. If you ever face that decision, it helps to understand how professionals decide what can be saved after a building fire.

Business continuity benefits as well. When less of a site is damaged, operations can restart sooner, staff return to work faster, and customers face shorter disruptions.

Where Smoke Ventilation Matters Most

Every building needs a fire plan, but some spaces depend heavily on effective smoke control.

Warehouses and factories, where large open volumes allow smoke to spread quickly.
Shopping centres and atriums, which hold many people unfamiliar with exit routes.
Theatres and event venues, where crowds need clear, calm evacuation paths.
Stairwells and escape corridors, which must stay usable throughout an emergency.
Heritage buildings, where protecting irreplaceable structures matters alongside speed.

Local building codes set the minimum requirements for each type of space. In Australia, that means the National Construction Code and related standards, while Europe relies on standards such as EN 12101-2 for natural smoke and heat exhaust ventilators.

Keeping Systems Ready for the Day They're Needed

Even the best equipment only helps if it works when a fire starts. Regular inspection and testing should be part of every building's maintenance schedule.

Here are a few habits that keep smoke ventilation reliable.

Book routine inspections with qualified technicians and keep clear service records.
Test automatic triggers and manual overrides, not just the vents themselves.
Keep low-level air inlets clear of stock, pallets, and furniture.
Review the system whenever a building changes use or layout.
Train staff on what the vents do and how evacuation routes work.

Technology works best alongside people who take safety seriously. Building a safety-first mindset across your workplace means drills, clear responsibilities, and regular conversations about risk, not just equipment on the roof.

Final Thoughts

Fire safety isn't only about putting flames out. It's about giving people time, visibility, and breathable air so they can get out safely, while reducing harm to everything left behind.

Intelligent smoke control does exactly that, quietly and automatically. For business owners, well-designed, well-maintained systems rank among the smartest investments for protecting staff, customers, and the property they share.

Creative Birthday Surprise Ideas for Someone Special

2026-10-22T04:18:55.147-04:00

Birthdays are a chance to show the people we love how much they mean to us. But after years of celebrations, it can be hard to come up with something fresh. Dinner and a gift are lovely, but sometimes you want to create a moment that feels truly special, something that makes the birthday person stop, smile and remember it for years to come.

The best birthday surprises don't have to be extravagant. They're thoughtful, personal and tailored to the person being celebrated. Sometimes it's a carefully planned day out, and other times it's a simple gesture, like arranging birthday flower delivery to their workplace so they're greeted by a beautiful bouquet in front of colleagues.

Here are some creative ideas to help make the next birthday unforgettable:

Start the day with a surprise

A birthday surprise doesn't have to wait until the evening. Starting the day on a high note can set the tone for a memorable celebration.

Prepare breakfast in bed with their favourite foods, decorate the kitchen with balloons and streamers before they wake up or leave a series of notes around the house leading to a small gift.

Flowers delivered first thing in the morning are another lovely way to begin the day, whether they're at home or at work.

Plan a surprise experience

Experiences often create lasting memories. Think about what the birthday person enjoys and plan something around their interests.

A food lover might enjoy a cooking class, wine tasting or degustation dinner. An adventure seeker may love a hot air balloon ride, kayaking trip or day at a theme park. Someone who enjoys relaxing could appreciate a spa day, picnic in a beautiful park or weekend getaway.

Keep the destination secret until the last moment to add an extra element of excitement.

Make it about shared memories

For something deeply personal, collect messages from friends and family. Ask people to write down favourite memories, funny stories or reasons they love the birthday person, then place them in a decorated jar.

Alternatively, compile short video messages from loved ones into a montage. This can be especially meaningful for people who have friends and family living far away. These gifts often become treasured keepsakes long after the birthday has passed.

Think about an important memory you share with the birthday person. You might recreate your first date, revisit a favourite holiday destination or cook a meal from a special occasion. Recreating meaningful moments shows thoughtfulness and can bring back wonderful memories.

Host a themed celebration

A themed party can make a birthday feel extra fun. Choose a theme based on the person's interests, favourite decade, hobby or film. Ideas include a garden party, casino night, retro disco, picnic, beach barbecue or murder mystery dinner. Decorations, food, costumes and music can all tie into the theme. Surprise parties can be exciting, but make sure the birthday person enjoys surprises. Some people prefer to know what's happening.

Create a scavenger hunt

A scavenger hunt can turn a birthday into an adventure. Leave clues around the house, neighbourhood or city, with each clue leading to the next location. The final stop might reveal a gift, a dinner reservation or a gathering of friends and family. Personalise clues with inside jokes and shared memories to make the experience even more special.

Timing and logistics can make or break a surprise. If you're organising something involving other people, set up a group chat or shared document so everyone knows the plan, and choose one person to keep the birthday person occupied or on schedule. Have a backup plan for outdoor activities in case the weather turns. And double-check bookings the day before. Few things deflate a surprise faster than arriving at a restaurant to find the reservation has gone missing.

Personalised gifts and small gestures

Personalised gifts show that you've put thought into choosing something meaningful. Ideas include custom jewellery, engraved keepsakes, photo books, artwork featuring a meaningful place or a framed map of where you met. Pairing a gift with a handwritten card can make it even more memorable.

Not every birthday surprise needs a big budget. Small gestures can be just as meaningful. Write a heartfelt letter, cook their favourite meal, take over their chores for the day or plan a cosy movie night with their favourite snacks.

The best birthday surprises come from understanding what makes someone happy. Whether you choose flowers, a surprise outing, a personalised gift or a simple heartfelt gesture, the effort and thought behind it are what make the celebration truly special.

Selenium Grid Online vs. Self-Hosted: Parallel Cross-Browser Execution

2026-10-21T14:15:19.762-04:00

Choosing between a cloud-hosted Selenium Grid online and a self-hosted grid is one of the earliest infrastructure decisions a QA team makes, and it shapes test execution speed, cost, data security posture, and long-term operational overhead for years. The right answer depends on the nature of what is being tested, the regulatory environment the organization operates in, and the engineering capacity available to maintain infrastructure. This article provides a decision framework covering parallel execution optimization, cross-browser coverage gaps, and the practical trade-offs between the two deployment models.

The Core Trade-Off: Elasticity vs. Control

Cloud-hosted Selenium Grids provide infrastructure that scales to thousands of parallel sessions without any provisioning work from the QA team. The provider manages hardware, operating systems, browser installations, and network configuration. A team that needs 500 concurrent sessions for a nightly regression run can access that capacity on demand and pay only for the time used. When the run completes, the capacity releases and the cost stops.

Self-hosted grids invert every one of these advantages. The team owns the hardware, manages the browser lifecycle, configures the network, and patches the operating system. In exchange, the team gains complete control over what enters and leaves the test environment, near-zero latency between the test runner and execution nodes, and a fixed, predictable cost that does not scale with test volume.

The table below maps the key decision factors across both models.

Factor	Cloud Grid	Self-Hosted Grid
Parallel session capacity	Elastic: scale to thousands on demand; no pre-provisioning required	Fixed: bounded by provisioned hardware; scaling requires procurement
Data residency	Traffic traverses provider network; requires DPA; limited for regulated data	All traffic stays inside corporate network; meets strict data residency rules
Execution latency	Public internet round-trip adds latency per WebDriver command	Near-zero latency; test runner and nodes share local network
Browser version management	Provider handles updates; teams pin versions via capability strings	Team manages browser installs; requires container image pipeline for consistency
Infrastructure maintenance	None: provider owns hardware, OS, network, and browser updates	High: team owns hardware, OS patching, network config, and browser lifecycle
Cost model	Variable: per-minute or per-session pricing scales with usage	Fixed: hardware capex plus ongoing engineer time for maintenance

Parallel Execution: How Each Model Scales

Parallel execution is where the cloud model's elasticity produces its most tangible advantage. A self-hosted grid with five nodes at 40 sessions each caps parallel capacity at 200 sessions. Adding capacity requires ordering, racking, configuring, and registering new hardware, a process that takes days or weeks. A cloud grid scales to 500 sessions by changing a concurrency setting in a configuration file.

The cloud model's elasticity particularly benefits two scenarios. Nightly regression runs benefit from burst capacity: the run completes in a fraction of the time it would take on fixed infrastructure because it can distribute work across far more parallel nodes. CI pipelines with variable commit rates benefit from elastic scaling that absorbs PR submission spikes without building a queue.

Self-hosted grids optimize parallel execution differently. Because all sessions run within the corporate network, the round-trip latency per WebDriver command is microseconds rather than tens of milliseconds. For large test suites with many commands per session, this latency reduction can make a self-hosted grid faster than a cloud grid for the same number of parallel sessions, even if the cloud grid has more sessions running concurrently. Teams running identical suites on both architectures consistently measure five to ten times faster command execution on local infrastructure.

Cross-Browser Coverage: Closing the Gap

Cross-browser coverage is the dimension where cloud grids most decisively outperform self-hosted setups. Maintaining a self-hosted grid that covers Chrome, Firefox, Edge, and Safari across multiple versions and operating system combinations requires a browser version matrix that grows with every browser release cycle. Chrome ships every four weeks. Firefox ships monthly. Each new version potentially changes rendering behavior and requires regression testing before it is approved for the grid.

A cloud provider maintains thousands of browser and OS combinations and handles version updates automatically. A team that wants to run their suite against Chrome 124, Chrome 125, Firefox 127, Edge 124, and Safari 17.4 on both Windows 11 and macOS Ventura can do so with capability strings rather than infrastructure procurement. A self-hosted grid would require separate node images for each OS and browser combination, plus the infrastructure to run them.

The practical compromise for teams that require on-premise deployment for data compliance reasons is a tiered coverage strategy. The on-premise Selenium grid handles the primary browser and OS combination used by the majority of the application's users. Cloud execution handles the long tail of browser and OS combinations for exploratory cross-browser coverage runs that do not touch sensitive data. This hybrid model captures the coverage breadth of cloud grids without routing regulated data through external infrastructure.

Optimizing Parallel Execution on Cloud Grids

Running tests in parallel on a cloud grid produces diminishing returns if the test suite is not structured for parallel execution. Three practices maximize the value of cloud parallel capacity.

Test isolation: Each test must be fully independent, creating its own test data and cleaning up after itself. Tests that share state through a database or through global session cookies produce intermittent failures when run in parallel because one test modifies shared state while another test is reading it. Isolating tests at the data level is the prerequisite for safe parallelization.
Session reuse: WebDriver session creation is the most expensive operation in a browser test. Starting a new browser session for every test multiplies setup time by the test count. Grouping tests that run against the same URL and user state into a shared session, and resetting to a known state between tests without restarting the browser, reduces total execution time substantially for suites with many short tests.
Capability-based routing: Use WebDriver capabilities to specify the minimum browser version and OS combination each test requires, rather than specifying an exact version. This allows the cloud grid's session router to assign each test to the most available node matching the capability, rather than queuing behind a specific node. Tight capability specifications create artificial bottlenecks in cloud grids where capacity is abundant.

Cost Optimization on Cloud Grids

Cloud grid costs scale with test volume in ways that self-hosted costs do not. A team that runs 10,000 test sessions per month pays ten times more than a team that runs 1,000, even if the test suite is structurally identical. Three practices reduce cloud grid costs without reducing coverage.

Test impact analysis is the highest-leverage cost reduction. Running only the tests relevant to a code change rather than the full regression suite reduces cloud session consumption proportionally. If a PR changes only the payment module, there is no value in running tests for the user profile module in that PR's pipeline run. AI-powered test impact analysis tools identify which tests are relevant to a given code change based on code coverage data and historical failure correlation.

Execution scheduling separates time-sensitive runs from batch runs. PR pipeline tests require fast feedback and justify cloud parallel capacity at a premium. Nightly regression runs do not need to be completed before the engineering team arrives in the morning. Scheduling batch runs during off-peak cloud pricing windows (where applicable) and allowing them to run at lower parallelism reduces cost without affecting PR feedback latency.

TestMu AI (formerly LambdaTest) addresses cloud execution cost and speed together through HyperExecute, its parallel test orchestration engine. HyperExecute distributes test execution across concurrent cloud environments, cutting overall suite run times by up to 70 percent compared to sequential cloud execution. Its smart test distribution algorithm groups tests by execution time to minimize idle capacity between test completions, improving parallelization efficiency. For teams running large Selenium suites on cloud infrastructure, HyperExecute's cost-per-test efficiency typically outperforms standard cloud grid pricing for the same coverage.

Common Questions About Grid Selection

Can you migrate a self-hosted grid to the cloud without rewriting tests?

Yes. Selenium WebDriver tests are grid-agnostic: the test script points at a RemoteWebDriver endpoint, and the grid handles session routing. Migrating from self-hosted to cloud requires updating the RemoteWebDriver URL to point at the cloud provider's hub and adding authentication credentials to the capabilities or request headers. The test code itself does not change. Teams that have hardcoded internal grid URLs in test configuration rather than in test code can migrate with a single configuration change.

What happens to self-hosted grid tests during browser major version releases?

Browser major version releases occasionally break existing WebDriver session handling or change the behavior of CSS or JavaScript features that tests rely on. A self-hosted grid gives the team control over timing: the team can delay a browser update until the suite has been validated against the new version. A cloud grid that automatically updates browsers can expose tests to breaking browser changes without warning. The standard mitigation on cloud grids is to pin the major browser version in capabilities until the team has validated compatibility, then lift the pin.

Advanced Visual Testing: Animations, Responsive Layouts, and Localization

2026-10-21T14:02:25.121-04:00

Basic visual regression testing covers static pages in a single locale at a fixed viewport size. Advanced visual testing extends coverage to three scenarios that basic setups miss entirely: animated UI elements that change between captures, responsive layouts that shift dramatically across viewport breakpoints, and localized interfaces that render different text lengths and bidirectional scripts depending on the user's locale. Each scenario requires a distinct technical approach. This article explains how to handle all three reliably at scale.

The Challenge of Animated UI Elements

CSS animations and JavaScript-driven transitions are the most common source of visual testing false positives and missed regressions. A screenshot captured while an animation is mid-state produces a different image than a screenshot captured when the animation has completed, even if the final rendered state is identical on both runs. A visual test that consistently captures at different animation phases will flag meaningless differences on every run.

The standard technique for handling animations is to freeze all CSS animations and transitions before the screenshot executes. Injecting a style tag that sets animation-duration and transition-duration to zero seconds on all elements prevents mid-state captures regardless of when in the page lifecycle the screenshot command runs. This approach works for CSS-driven animations and should be applied globally in the test setup hook rather than selectively per test, to prevent animated elements outside the focus area from introducing noise.

JavaScript-driven animations and WebGL transitions require a different approach. They cannot be frozen by overriding CSS properties. The options are to wait for the animation to complete before capturing, to hide the animated element during the screenshot and exclude it from the comparison, or to capture only the static portions of the page around the animated element. Waiting for completion requires knowing the animation duration, which may not be consistent across environments. Hiding the element is the more reliable choice for animations that are not themselves the subject of the visual test.

Loading spinners and skeleton screens present a specific variant of this problem. A page that uses a skeleton screen while data loads may render the skeleton in some test runs and the loaded content in others, depending on how long the data fetch takes in the CI environment. The correct fix is to mock the data API to return instantly and consistently, so the skeleton state never appears in a visual test capture. This applies the same network interception principle used for dynamic content masking, but specifically to prevent loading state captures rather than data variance.

Responsive Layout Testing Across Breakpoints

Responsive layout testing validates that a UI renders correctly at every viewport size where its CSS media queries trigger a layout change. This is not the same as running a test at a few arbitrary viewport widths. Effective responsive testing identifies the specific pixel widths where layout transitions occur and validates the layout immediately before and after each transition.

Most CSS design systems define a small set of breakpoints, typically four to six, at which the layout changes meaningfully. Common breakpoints are 375px (iPhone base), 768px (tablet), 1024px (small desktop), 1280px (standard desktop), and 1440px (large desktop). Testing at these exact widths, plus one pixel below each threshold, confirms that the layout transitions at the intended point and that the layout on either side of the threshold renders correctly.

The risk of testing only at canonical breakpoints is missing regressions between them. A component that works at 768px and 1024px may have a layout collapse at 900px if a flex container wraps unexpectedly at that width. Responsive visual testing programs that cover only the canonical breakpoints accumulate these between-breakpoint regressions. Extending coverage to include a midpoint between each pair of canonical breakpoints (for example, 896px between 768px and 1024px) catches the most common between-breakpoint failures without multiplying test count excessively.

Viewport-specific baselines: Store a separate baseline image for each viewport width. A change that affects only the mobile layout should fail the mobile viewport baseline without failing the desktop baseline.
Component-level responsive testing: Test navigation menus, cards, forms, and tables in isolation at each breakpoint rather than capturing full pages. Component-level captures are faster, produce smaller diff areas, and make failure analysis easier when a responsive regression appears.
Container query coverage: Modern CSS increasingly uses container queries rather than viewport-based media queries. Container queries respond to the container element's width rather than the viewport width. Testing components at multiple container sizes requires resizing the component's parent element in the test, not the viewport.

Visual Testing for Localized Interfaces

Localized interfaces introduce two visual testing challenges that English-only test suites never encounter: text length variation and bidirectional script rendering. Both require deliberate test architecture to handle correctly.

Text length variation occurs because translations expand the space required to render equivalent content. German text typically runs 20 to 30 percent longer than English. Finnish and Dutch expand similarly. Japanese and Chinese text is often shorter. A button labeled 'Checkout' in English may render 'Zur Kasse' in German, which is longer and may overflow a fixed-width button or wrap unexpectedly in a flex container. A visual regression that only runs in English will never detect this overflow.

Pseudo-localization is the most efficient technique for catching text expansion layout regressions before translations are available. Pseudo-localization replaces every translatable string with a version that is 30 to 40 percent longer and includes accented characters and Unicode characters from multiple scripts. Running the visual test suite against the pseudo-localized version of the application catches all layout overflow, truncation, and wrapping regressions that would occur in any target locale with long translations, without requiring actual translated content.

Bidirectional (RTL) script rendering requires a completely separate set of baselines. Arabic, Hebrew, Farsi, and Urdu use right-to-left text direction, and a properly internationalized application should mirror its layout when RTL mode is active. Navigation that sits on the left in English moves to the right in Arabic. Back arrows point in the opposite direction. Column order in tables reverses. A visual test suite that only validates left-to-right rendering will miss any RTL layout regression.

RTL visual tests require the application to be loaded in RTL mode before the screenshot is captured. For web applications, this means setting the HTML document's dir attribute to rtl and loading the appropriate locale's string file. The screenshot captures the mirrored layout, and the RTL baseline reflects the correct mirrored state. Any subsequent change that breaks the RTL mirroring appears as a visual diff against that baseline.

Building a Locale-Aware Visual Test Matrix

A locale-aware visual test matrix extends the standard two-dimensional test matrix (pages times viewports) to a third dimension (locales). For most applications, it is not practical to run the full page and viewport matrix for every supported locale. The locale dimension should focus visual testing effort where locale-specific regressions are most likely to appear.

The highest-risk locale candidates for visual testing are the locale with the longest average translated string length (typically German, Finnish, or a similarly verbose language), one RTL locale (Arabic or Hebrew) to validate bidirectional layout, and the application's highest-traffic non-English locale to catch translation-specific rendering issues in the strings most users will see.

For each locale in the visual test matrix, the recommended baseline strategy is to capture baselines using fixture translation data rather than real translated strings. Real translations change whenever translators update a string. Fixture data is version-controlled alongside the test suite and changes only when explicitly updated. This makes the baseline stable across builds that have no code changes but receive updated translations, preventing a translation update from failing every visual test in the locale suite.

Common Issues and How to Fix Them

Why does freezing CSS animations still produce inconsistent screenshots?

If CSS animation freezing does not eliminate animation-related false positives, the remaining inconsistency is likely from JavaScript-driven animations that bypass CSS duration properties. Check whether the component uses requestAnimationFrame or a JavaScript animation library such as GSAP or Framer Motion. These libraries drive animation through JavaScript timers rather than CSS, and they require explicit mocking or waiting to stabilize. The practical fix is to expose a test mode flag in the application that disables JavaScript animations in addition to CSS animations, and to activate that flag in the visual test environment before screenshots are taken.

How do you validate responsive layout without running the full suite at every breakpoint?

Full-suite responsive coverage is expensive. A pragmatic approach runs a curated set of layout-critical components at every breakpoint rather than every page. Components that contain navigation, data tables, form layouts, and card grids are the highest-risk elements for responsive regressions. Running these components across all defined breakpoints in a component-level visual test catches the vast majority of responsive regressions. Full-page captures at breakpoints are reserved for critical pages such as the homepage, the checkout flow, and the authentication pages, where full-page composition matters beyond individual component layout.

SmartUI by TestMu AI (formerly LambdaTest) supports viewport-specific baselines natively. Each capture at a different viewport width is stored as a distinct baseline. The visual AI testing Precise Shift Detection distinguishes between expected responsive layout changes and unintended element shifts at the same viewport. The Figma-to-web comparison capability extends this to design validation: designs for each responsive breakpoint in Figma can be compared directly against the live application rendering at the corresponding viewport, closing the gap between design intent and production implementation.

For teams managing responsive visual coverage across many components and locales, SmartUI's parallel execution across 3,000-plus browser and device combinations makes comprehensive coverage practical within CI time budgets.

Next Steps: Combining Animation, Responsive, and Locale Coverage

Advanced visual testing techniques are most powerful when they are combined into a single coordinated test matrix rather than implemented as three separate uncoordinated test efforts. A combined matrix that covers the critical pages at canonical breakpoints, in two to three locale configurations, with consistent animation stabilization, provides comprehensive visual coverage with a manageable test count.

Start by auditing the current visual test suite for animation handling. If the suite does not freeze animations globally before every capture, that is the first fix. Inconsistent animation handling makes all other visual test results unreliable because the source of a diff is ambiguous.

Add responsive breakpoint coverage next. Identify the breakpoints defined in the CSS design system and extend the existing visual captures to run at each breakpoint rather than at a single viewport width. This requires only a viewport loop in the test configuration and doubles or triples the visual coverage without new test authoring.

Add locale coverage last. Start with pseudo-localization to catch text expansion regressions without requiring translation infrastructure. Add the RTL locale once pseudo-localization coverage is stable. Extend to the highest-traffic non-English locale when the team has the baseline management capacity to maintain locale-specific snapshots across releases. This incremental approach ensures that each tier of visual coverage is reliable before the next tier is added on top of it.

The Future of Test Management: AI, Flaky Tests, and Analytics

2026-10-21T13:48:30.908-04:00

Test management is undergoing its most significant architectural shift since the move from spreadsheets to dedicated platforms. The shift is from passive record-keeping to active quality intelligence. A traditional test management system stores test cases, records execution results, and generates coverage reports on request. An AI-driven test management system analyzes historical execution data to predict where defects will surface, identifies which tests are flaky before they poison CI pipelines, and ranks the test cases most likely to catch regressions in a given code change, all without a QA engineer manually curating the execution order. This article explains how AI-driven prioritization, flaky test detection, and predictive analytics work in practice and what teams need to do to benefit from them.

Why Traditional Test Management Reaches Its Limits

Traditional test management works well when test suites are small, release cycles are long, and the relationship between requirements and tests changes slowly. None of those conditions hold for teams shipping software multiple times per week.

As test suites grow past a few thousand cases, running the full regression suite on every PR becomes impractical. A suite that takes three hours to run cannot gate a ten-minute PR feedback loop. Teams respond by creating curated smoke suites and regression subsets, but the curation is manual and therefore always stale. The smoke suite captures what was important six months ago, not what matters for the current sprint's changes.

At the same time, test suite reliability degrades at scale. The absolute number of flaky tests grows with the total test count even if the percentage stays constant. A 2 percent flaky rate in a 500-test suite means 10 flaky tests. In a 5,000-test suite, it means 100 flaky tests. A CI pipeline with 100 tests that fail intermittently trains developers to ignore test failures, which defeats the purpose of having a CI gate.

AI-driven test management addresses both problems. Intelligent prioritization replaces manual smoke suite curation with a model that selects the highest-risk tests for each code change. Automated flaky test detection identifies unreliable tests before developers learn to distrust the suite as a whole.

AI-Driven Test Prioritization in Practice

AI-driven test prioritization uses machine learning models trained on historical execution data to rank test cases by their probability of catching a defect in a given code change. The model combines three data sources: code coverage data that maps source code files to the tests that exercise them, defect history that tracks which tests have historically caught bugs in which modules, and code change analysis that identifies which files are modified in the current PR.

When a developer opens a PR that modifies the payment processing module, the prioritization model retrieves all tests that have historically caught defects in payment processing, filters to those that cover the specific files changed in the PR, and ranks them by recent failure rate (more recently failing tests carry more diagnostic value). The CI pipeline runs the top-ranked subset rather than the full suite. For most code changes, this subset captures 90 percent or more of the failures that the full suite would find, in 20 to 30 percent of the execution time.

The model improves over time. Each PR that slips a defect past the prioritized subset provides a training signal: a test that was ranked too low and should have been included. Each prioritized subset that catches all failures provides positive reinforcement for the current ranking model. Teams that have been running AI-driven prioritization for three to six months typically see test selection accuracy above 85 percent, meaning fewer than 15 percent of defects slip past the selected subset into the full regression run.

Flaky Test Detection and Remediation

A flaky test is a test that produces inconsistent results across identical code states. It passes on one build and fails on the next without any code change that should explain the difference. Flaky tests are the leading cause of CI pipeline distrust: when developers cannot distinguish a real failure from a flaky failure, they merge code without investigating test failures, and the CI gate loses its value.

Automated flaky test detection analyzes execution history across builds to identify tests with high result variance. A test that passes 8 times and fails 2 times in 10 consecutive builds on the same code version is flaky. A detection system flags it, categorizes it, and routes it to the QA team for remediation rather than allowing it to appear as a normal failure in CI.

Flaky test categorization is as important as detection. The three most common root causes require different remediation strategies. Timing-dependent flakiness, caused by race conditions, animation timing, or network response variability, requires explicit wait conditions or mock stabilization. State-dependent flakiness, caused by tests that assume a clean database state or a specific session state that prior tests may have modified, requires better test isolation. Environment-dependent flakiness, caused by differences in rendering, font loading, or third-party API behavior between CI runs, requires environment normalization or dependency mocking.

Categorizing flaky tests by root cause at scale requires more than manual inspection. AI-powered flaky test analysis groups flaky tests by failure message pattern, execution time variance, and correlation with other failing tests to infer the root cause category. A set of tests that all fail with TimeoutException when the CI runner is under load share a timing-dependent root cause. A set that fails only on runs that execute after a specific other test shares a state-dependent root cause. The categorization guides remediation priority and approach without requiring manual investigation of every failing test.

Predictive Analytics: Quality Before the Test Runs

Predictive analytics moves test management from reactive to proactive. Instead of analyzing which tests failed after a build runs, predictive models analyze code changes before execution to forecast where defects are likely to surface. This shifts the quality decision from 'which failures happened?' to 'which areas carry the most risk?'

The inputs to a predictive defect model include code complexity metrics, code churn rate (how frequently a module changes), historical defect density per module, code review patterns (modules with consistently poor review coverage have higher defect rates), and the qualifications of the developers making the change relative to the module they are modifying. Combining these signals produces a module-level risk score that guides where testing effort should be concentrated.

According to the ThinkSys QA Trends Report 2026, 77.7 percent of organizations now use or plan to use AI in QA. The primary use cases are test data creation and test case formulation, but predictive analytics and intelligent prioritization are the capabilities that produce the most direct quality impact for teams with mature automation estates. Teams that have implemented predictive analytics report 40 percent reductions in regression testing time with equal or better defect detection rates compared to running the full suite.

What AI Test Management Looks Like in Practice

The capability description of AI test management is abstract until it is grounded in concrete workflow changes. Three workflow changes distinguish genuine AI-driven test management from a traditional platform with an AI marketing label.

Test selection is automatic, not manual. The QA lead does not curate the regression subset for each sprint. The platform analyzes the sprint's code changes and selects the test subset based on coverage analysis and historical failure data. The QA lead reviews and approves the selection rather than creating it from scratch.

Failure triage is assisted, not manual. When the CI run completes and shows 15 failures, the platform categorizes them by root cause, groups duplicates, and surfaces the three unique defects that caused all 15 failures. The QA engineer verifies the categorization rather than manually reading fifteen stack traces to identify three unique root causes.

Coverage gaps are surfaced proactively. When a new feature adds code to a module that has no linked test cases, the platform flags the gap and suggests test cases based on the module's code structure and similar modules' test coverage. The QA engineer reviews and approves rather than discovering the gap during a sprint review.

TestMu AI (formerly LambdaTest) is building toward this vision through its KaneAI and Test Intelligence capabilities. KaneAI generates and evolves test cases from natural language descriptions of requirements, reducing the manual effort of authoring tests for new features. The Test Intelligence layer provides flaky test detection, failure categorization, and coverage analysis integrated with the execution pipeline. For teams ready to move from a reactive test management tool to AI-driven quality intelligence, the platform provides a migration path that starts with execution analytics and progressively adds prioritization and predictive capabilities.

Building the Data Foundation for AI Test Management

AI test management capabilities are only as good as the historical data they train on. A team that has been running automated tests for two months has too little history for reliable failure prediction. A team with two years of execution data across ten thousand builds has enough signal for highly accurate prioritization models. Building the data foundation is therefore a prerequisite investment, not an optional one.

The data that matters most is structured execution history: for each test run, which test cases executed, what the result was (pass, fail, skip, or flaky), how long the run took, and what code version was under test. This data should be stored in a queryable format rather than in flat build logs. Most test management platforms store this data natively. The implementation requirement is ensuring that automated test results are consistently ingested into the platform from every CI run rather than only from scheduled regression runs.

Teams that have been running automation for more than a year with consistent result ingestion are ready to start evaluating AI test management capabilities today. Teams that are starting fresh should prioritize consistent result ingestion and structured tagging of test cases with requirement IDs and module ownership from the beginning. The data collection discipline established now determines the accuracy of the AI models that will run on that data twelve months from now.

Moving Flats in London? A Practical Guide to Getting Rid of Bulky Items

2026-10-21T08:05:20.678-04:00

The sofa won't fit the new place, the mattress is past it and the fridge belongs to nobody. Here's how to clear bulky items legally before moving day, and what it costs in London.

In 2024/25, Lewisham Council picked up 1,062 more tonnes of fly-tipped waste than it collected through its own booked bulky waste service, according to an October 2025 Cabinet report. The report says fly-tipped mattresses and fridge-freezers “significantly exceed the number collected through the official booking system.” It's a clear sign that bulky waste removal in London is something many households leave until it's too late.

That borough is not unusual. London has “the highest rates of fly-tipping in the country – 53 incidents per 1,000 people each year,” a London Assembly Environment Committee paper noted on 14 October 2026. London councils dealt with 481,088 incidents in 2024/25, up 8.2% on the year before and 48% since 2018-19, the London Assembly Research Unit reports.

Moving home creates exactly this kind of waste. About 1.8 million households in England moved in the 12 months before the latest English Housing Survey, and around half of those moves were into or out of private rented homes. If you're one of them, here's how to deal with the big items properly.

Why bulky items are the problem

Across England, single-item fly-tips such as a dumped sofa or mattress rose 12% to 201,000 in 2024/25, Defra figures show. Pavements and roads were the most common place for fly-tipping, at 37% of all incidents.

That matters for anyone leaving a wardrobe “out for someone to take”. Furniture left on the street without arrangement counts as fly-tipping, however good the intention.

The most recent London-specific breakdown, from 2016/17, found almost half (47%) of fly-tips in the capital were bulky household items such as mattresses, furniture and white goods (Keep Britain Tidy and LEDNet, 2018). The same study found people in smaller homes were more likely to fly-tip bulky items, and that bulky waste services were widely seen as costly and inconvenient. That research is now eight years old, but no newer London survey of its kind exists.

Start six weeks out: sort what stays, sells and goes

Most London moves are planned. Give yourself time to work through the options in order:

Sell or give away anything in good working order.
Donate furniture and electricals to a reuse charity.
Use retailer take-back for old appliances you're replacing.
Book a council collection for what's left, as early as possible.
Hire a licensed waste carrier for large, mixed or last-minute loads.

Option 1: Donate or reuse it

Reuse charities give items a better chance of a second life than a council pick-up. An older academic study found reuse organisations reused about 85% of the bulky items they received, against 2-3% of items collected by councils (University of Southampton thesis). That data dates from 2007, and council reuse has likely improved since, but the gap is a useful guide.

More of your furniture may qualify than you'd think. A WRAP study found 32% of bulky items taken to recycling centres were reusable as they were, rising to 51% with slight repair (Resource, reporting WRAP). Again, this is older data (2012).

Charities such as the British Heart Foundation offer free collection of saleable furniture and electricals. Most need clean items with intact fire safety labels, so check the label before you book.

Option 2: Book a council bulky waste collection

Almost every council charges for this service, and prices vary widely between neighbouring boroughs.

Council	Price	Items included	Source
Lewisham (from December 2025)	£5 per item; £25 per fridge or freezer	Per item	Lewisham Council, June 2026
Haringey (2026/27)	£20	Up to 4; £10 each for items 5 to 10	Fees and charges 2026/27
City of London (2025/26)	£42.50	Standard collection	Committee charges report 2025/26
Southwark (2026/27)	£37 residents, £92.80 landlords	Up to 5	Fees and charges 2026/27

Prices change, usually each April. Check your council's booking page before you book.

Lewisham's change is worth watching. The council replaced a flat £42 charge for up to four items with a £5 per-item fee, and cut its fridge-freezer charge from £60 to £25, accepting a “potential overall annual loss of income of £160k” (Cabinet report). Bookings rose 91% between September 2025 and May 2026, and reported fly-tipping fell 4% in January to March 2026 compared with the previous quarter, the council says. One quarter of data doesn't prove the cheaper fee caused the drop.

Two things catch movers out. Landlords can pay more: in Southwark, the same five items cost a landlord about two and a half times the resident rate. And the collection date may not line up with your tenancy end, so book as soon as you know your moving date.

Problem items: sofas, mattresses and fridges

Sofas and armchairs

Upholstered seating has its own rules. Waste sofas, armchairs, sofa beds, dining chairs with padding, futons, bean bags and large cushions are assumed to contain persistent organic pollutants (POPs). “You must incinerate waste domestic seating containing POPs or mixed waste containing it,” government guidance states. It can't go to landfill.

That's why some councils book sofas separately from other bulky items. It also explains why a skip is the wrong place for a sofa.

A sofa in good condition isn't necessarily waste, though. Separate guidance allows reuse, including resale, if the item is intended for reuse, needs no more than minor repair and carries appropriate fire safety labels. Donating it is often the best route.

Mattresses

Mattresses are not covered by the sofa rules, but they're one of London's most dumped items. Lewisham received 38,137 fly-tipping reports through its app in 12 months, and mattresses and bags of rubbish were the most common, at just over 4,000 reports each. Nationally, only 24% of the estimated 6.4 million mattresses reaching end of life in 2021 were recycled, the National Bed Federation reports.

If you're buying a new mattress for the new flat, ask the retailer whether it collects the old one.

Fridges, washing machines and other electricals

Retailers that sell electrical goods must offer to take back your old item when you buy a new one that does the same job. In-store take-back must be free, you get at least 28 days to bring it in, and a retailer collecting from your home may charge only for transport, according to GOV.UK guidance updated on 5 October 2026.

Moving is also a good time to clear the drawer of old chargers and gadgets. London throws away around 12,400 tonnes of electricals a year, Material Focus estimates.

Who you hand your rubbish to matters

You're legally responsible for your household waste until it reaches someone authorised to take it. If an unregistered collector dumps it, you can be fined. Legislation raised the maximum duty of care fine for householders from £400 to £600, and the maximum fly-tipping fixed penalty from £400 to £1,000 (Runnymede Borough Council report). Lewisham has applied the £1,000 maximum since 1 April 2025.

The risk is real. Around 40% of fly-tipping incidents across the country “are reported to have been caused by illegal operators” who collect waste and dump it, the London Assembly Environment Committee noted in July 2026. That's an estimate, not a measured figure. London councils took 230,948 fly-tipping enforcement actions in 2024/25, up 12.9%.

Penalties are getting tougher. Under the Crime and Policing Act 2026, courts will be able to add up to 9 penalty points to the driving licence of anyone convicted of fly-tipping, Defra said in a written answer on 19 May 2026.

Before you pay anyone to take your waste:

Check their waste carrier registration on the Environment Agency public register.
Ask where the waste will go.
Get a waste transfer note or written receipt.
Be wary of cash-only offers with no paperwork.

The registration system itself is changing. New regulations made on 22 July 2026 will move waste carriers in England onto environmental permits, with the main changes taking effect on 22 July 2027, CIRCULAR Online reports. Until then, the public register check is the one to do.

When a licensed collection is the easiest option

A private service makes most sense when:

you have more items than your council's cap allows
the council's next slot is after your keys go back
you're a landlord facing a higher council rate
your load mixes furniture, appliances, bags and boxes
you have no car or van to get to the tip

FAQs

Can I leave furniture on the pavement for someone to take?

No. Unless your council has arranged a collection, leaving it on the street counts as fly-tipping, and you could be fined up to £1,000.

How much does council bulky waste collection cost in London?

It depends on the borough. Lewisham charges £5 per item, Haringey £20 for up to four items, and the City of London £42.50 for a standard collection.

Can I donate an old sofa?

Often, yes. Government guidance allows good-condition upholstered seating to be reused if it needs no more than minor repair and has appropriate fire safety labels.

Will the shop take my old fridge?

If you're buying a replacement, retailers must offer like-for-like take-back. In-store take-back is free; home collection can carry a transport charge.

5 Diamond Rings Defining the New Rules of Personal Style

2026-10-21T04:17:00.000-04:00

A ring may be small, but the impression it leaves can be anything but. It can become the finishing touch that pulls an outfit together, add character to your everyday look, or quietly reflect your personality.

Since rings stay visible through almost every gesture, the right one can become a subtle signature of your personal style. This is where diamond rings can add a distinctive touch to the way you express yourself.

Let’s discover five diamond ring designs and find the one that matches you best.

5 Diamond Rings to Express Your Individual Style

1. Geometric Diamond Rings With a Brilliant Solitaire

A solitaire ring features one prominent diamond at the centre, making it the main focus of the design. The centre diamond can come in different shapes, including round, oval, pear, emerald, and princess cuts.

In this style, the stone is framed by angular metal elements that create a geometric outline around it. Straight lines and defined corners add structure, while the single centre diamond keeps the overall look focused and refined.

Styling tips:

Pair with structured or tailored outfits
Keep other jewellery minimal
Add a slim bracelet or bangle

2. Floral Diamond Rings With Geometric Accents

This design combines the appearance of a flower with structured shapes. The floral elements resemble petals, with curved sections creating a softer appearance around the centre.

These curves are paired with geometric details, such as defined edges and organised shapes. Small diamonds are placed across the design to add sparkle and highlight its different sections.

Styling tips:

Style with flowy dresses or blouses
Choose delicate earrings or pendants
Opt for soft or pastel outfits

3. Clover-inspired Diamond Rings With Soft Symmetry

This ring takes inspiration from a clover, a shape commonly recognised by its rounded sections arranged around a centre. Here, these sections come together to create a balanced, symmetrical pattern.

The rounded edges give the ring a softer appearance, while diamonds are added to the motif for subtle sparkle. The repeated shape also makes the design easy to recognise at a glance.

Styling tips:

Pair with simple, understated outfits
Match with rounded jewellery styles
Add a slim stacking band

4. Blossom-inspired Diamond Rings With Graceful Silhouettes

Inspired by the natural form of a blooming flower, this design features a soft and flowing silhouette. The elements spread out from the centre to create an open floral appearance.

Diamonds are incorporated into these petal-like sections, allowing the individual curves to catch the light. The rounded forms give the ring a softer look compared with designs based on sharp or angular shapes.

Styling tips:

Pair with flowing silhouettes
Choose delicate nature-inspired jewellery
Wear with neutral or monochrome outfits

5. Peacock-motif Diamond Rings With Statement Appeal

The specific ring design uses a peacock motif, meaning the shape and decorative details are inspired by the bird. Curved elements suggest the peacock's body and feathers, creating a more detailed pattern across the top of the ring.

Diamonds are positioned within these sections to bring attention to the intricate details. The larger, decorative motif gives the ring a bolder appearance than a simple band or minimal ring.

Styling tips:

Let the ring remain the focal point
Pair with solid-colour outfits
Style with festive or evening wear

What Should You Look For When Exploring Diamond Rings in India?

These are the things you should look for before buying a diamond ring:

Check the Diamond and Ring Details

If you are wondering what to look for when exploring diamond rings from a jewellery brand in India, start with the essentials. Check the diamond certification, carat weight, cut, clarity, metal purity, setting, and finish.

For gold, check the hallmark to verify its purity, such as 14K, 18K, or 22K. These details can help you compare different rings while finding a piece that matches your personal style.

Look at the Motif and Finish

For a distinctive motif such as a peacock, you may ask: How can I tell if a peacock ring with a diamond is genuine and well-made? Look at how clearly the peacock motif is defined, how evenly the diamonds are placed, and whether the details appear balanced from different angles.

A well-finished design should feel cohesive while allowing the motif to remain the focal point.

Understand the Carat of the Diamond

If you are exploring a 1 carat diamond, you may wonder what to consider and how its carat weight affects the price.

A higher carat weight generally means a larger diamond, which can create greater visual presence and influence the overall value of the ring. The final price can also vary based on the diamond's individual characteristics and the jewellery setting.

Let Your Diamond Ring Define Your Style

Your jewellery should feel like an extension of you. The right diamond ring can complement your personality and become a subtle signature of your style.

Explore different options, choose a piece that feels genuinely like you, and find something you will enjoy wearing for years to come. Discover diamond ring collections from brands like Indriya and find a piece that adds a distinctive touch to your jewellery collection.

Yelp's new AI will call restaurants for you and text back the wait time

2026-10-20T09:46:36.504-04:00

The Check for Me feature starts rolling out on iOS as part of Yelp's fall product release, which also brings OpenTable bookings to Yelp Assistant.

You're outside a restaurant, hungry, and the host stand is chaos. You could call. Or you could let Yelp's new AI agent do it. That's the pitch behind Check for Me, a feature Yelp began rolling out this week on iOS.

You would tap "Check wait time now" on an eligible restaurant's page, enter your party size and phone number, and Yelp's AI voice bot dials the restaurant, asks about the wait, and texts you the answer within minutes.

Credit: Yelp

It's only for walk-in restaurants in the U.S. at first, and Yelp hasn't named launch cities or an Android timeline, saying only that it's coming "in the coming months".

The feature is part of Yelp's 2026 Fall Product Release, which the company said includes more than 30 new features and updates.

Yelp Assistant, the company's AI chatbot, now integrates with OpenTable, giving users access to tens of thousands of restaurants across the U.S. for reservations without leaving the app. The assistant also picks up personalization from past chats and review history, can set recurring reminders through a feature called Keep Me Posted, and factors in real-time weather when recommending places to go.

"This release marks the next step in our journey to transform Yelp into an intelligent, more personalized experience that effortlessly adapts to each user's individual needs," said Akhil Kuduvalli Ramesh, Yelp's chief product officer.

The company hasn't detailed how Check for Me handles a restaurant that uses its own AI phone agent, a scenario TechCrunch flagged as a possible agent-to-agent breakdown.

Yelp also hasn't said whether the AI identifies itself as an AI on the call or how restaurants can opt out of these automated queries. And while the feature could cut down on routine wait-time calls for busy staff, Yelp hasn't shared how many restaurants are eligible or what happens when a restaurant doesn't pick up.

Yelp says Check for Me is rolling out "in the coming months" on eligible restaurant pages and in Yelp Assistant when walk-ins are possible.

Yelp has not said when Android users will get the feature or not.

Elon Musk Pledges One SpaceX Share for Every U.S. Child

2026-10-20T04:50:14.346-04:00

Elon Musk said Monday he will give one of his SpaceX shares to every child in the United States next year, a pledge with an estimated price tag of roughly $10.5 billion and almost no operational detail.

“Next year, I will give one of my SpaceX shares to every kid in America.”
“This will be the first of many.” Musk wrote on X at 3:57 a.m. Eastern Time.

Image Credit: DogeDesigner on X @cb_doge

The 18-word post did not say how the shares would be transferred, how a child’s eligibility would be verified, or what happens to Americans who turn 18 before the distribution. It also did not define “kid,” like the age, gender, etc and SpaceX did not publish a separate statement either.

Update: The funds will go into Trump Accounts.

The post also leaves an open question about which stock he would hand over as SpaceX’s filing gives public Class A shares one vote each and insider Class B shares ten. Musk did not say which class would go to the children.

Musk owns about 4.8 billion SpaceX shares, according to an August SEC filing. That figure rises to 6.4 billion including unvested awards and options, a 48.4% beneficial stake. Forbes puts his ownership at 38%.

The company listed on Nasdaq in June after raising $85.7 billion in the largest IPO on record, and the stock closed Friday at $146.03.

There were 72 million children under 18 in the U.S. as of July 1, 2025, census data show. At Friday’s close, one share for each would cost about $10.5 billion. A separate estimate of 73.1 million minors for this year puts the figure near $10.7 billion.

Elon Musk’s personal fortune is estimated at $1.1 trillion, so the gift works out to roughly 1% of his wealth.

The world's first trillionaire posted this after Anthropic said it would set aside 5% of its shares for a public benefit fund. It also followed President Donald Trump’s promise last month to give every American adult $5,000 if Republicans keep Congress in November’s midterms.

But minors generally cannot hold stock directly in the U.S. The shares would likely need to sit in custodial accounts managed by parents or in a trust. Musk has not said whether children born during 2027 would qualify, or whether the transfer would be structured to avoid a tax bill for families. He has previously said he opposes automatic inheritance of company shares by children, arguing control should go to people capable of running the business.

The announcement follows months of pressure from the White House. In a July CNBC interview, President Donald Trump said he expected Musk to put SpaceX stock into Trump Accounts, the federal child investment program:

“Well, I think that he will do that,” Trump said.

Musk had not publicly committed to anything by the following week, and those accounts receive a $1,000 Treasury deposit for each child born from January 1, 2025, through December 31, 2028. Musk’s post does not say whether his shares would go through those accounts or not.

One X user calculated the pledge would distribute about 0.5% of SpaceX’s shares and Axios reported the gift would be nearly double Michael Dell’s record-setting Trump accounts contribution.

We don't know whether a child must be a U.S. citizen, a resident, or simply present in the country and we need to wait for more on this from SpaceX or Elon Musk.

Apple Quietly Bought the Tech That Lets You Edit Notes Inside a Chord

2026-10-20T04:27:25.170-04:00

The European Commission's acquisitions register, a document most music producers never open, now carries a June 17 entry that explains why Samplab's website redirects to Apple.com.

The filing, posted under the Digital Markets Act, says Apple Inc. "through a subsidiary, will acquire certain assets of Samplab GmbH" and "offer employment to and hire certain Samplab employees".

Apple has not issued an official statement on the deal, and the purchase price was not disclosed.

Samplab built tools that let musicians treat recorded audio the way they treat MIDI. Its software could detect chords and keep them in key, match tempo and key across samples, split stems, and edit individual notes inside polyphonic recordings without flattening the original timbre. It ran as a desktop app and as VST3 and Audio Unit plugins, including for Logic Pro.

The company was founded in Zurich in 2020 by Manuel Fritsche and Gian-Marco Hutter, and its LinkedIn page listed no more than ten employees. Its website described the product as "music production for the age of AI" and promised users could "edit audio files as if you were working with MIDI".

Samplab announced its shutdown before the EU filing surfaced.

"After many years of working on Samplab, we have made the decision to pursue a new opportunity," the team wrote in a notice on its site.

Support for existing users and the ability to upload new audio ended on September 17, 2026. Subscribers with annual plans extending beyond that date were offered refunds for the remaining balance.

The site now redirects to Apple's homepage.

The EU disclosure does not identify which Apple products will use the technology

Logic Pro already includes Stem Splitter and Chord ID, and industry analysts expect Samplab's note-level polyphonic editing to be folded into Logic Pro or GarageBand. Though this is not a confirmed product plan.

This acquisition is not alone here, Apple has made a string of small AI audio deals.

Last week the same EU register revealed a non-exclusive technology licence and hiring arrangement with Huxe, a startup founded by former NotebookLM engineers.

The Samplab entry is structured as an asset purchase and partial hire, not a full company acquisition.

The Digital Markets Act requires companies designated as "gatekeepers" to report acquisitions involving core platform services, digital services and data collection to the Commission.

That disclosure rule is why a quiet June transaction became public in October.

Apple did not respond to a request for comment on the filing.

Sahara Ray, model and Justin Bieber's ex, dies at 34 (cause of death revealed)

2026-10-20T03:48:43.103-04:00

Sahara Ray, an Australian model and swimwear designer who was briefly linked to Justin Bieber, died on Sunday, October 18, at a hospital in the Los Angeles area. She was 34.

Her family confirmed the death in a statement to TMZ.

"She passed away last night surrounded by family at the hospital," the statement said. "She was so loved."

Photo credit: Sahara Ray / Instagram

Cause of death

The Los Angeles County medical examiner has not released an official cause of death. But the Daily Mail report revealed that Sahara Ray suffered a suspected fentanyl overdose on Friday night and was placed on life support before her family made the decision to remove it on Sunday afternoon.

The report cited a source close to the model. It remains unclear whether the overdose was accidental or intentional.

Sahara Ray's mother, Gretchen Bach, issued a separate statement:

"Sahara was an incredibly passionate and kind young woman who loved her family, fashion, her daughter Snow, and surfing," Bach said. "We're feeling the love that is pouring in from her cherished friends, fans and the many communities that she was part of around the world."

Sahara Ray gave birth to Snow in January 2026. She never publicly identified the child's father.

In her final Instagram post, from July, she wrote:

"My whole heart baby Snow."

The model's death followed a period of significant health challenges.

In 2024, Ray disclosed that she had been diagnosed with Guillain-Barré syndrome, a rare neurological disorder in which the immune system attacks the nerves.

Photo credit: Sahara Ray / Instagram

She said the condition developed after she was hit by an alleged drunk driver travelling at 80 mph.

"So basically, right now I'm paralyzed from the tops of my upper thighs all the way down to my feet. I can't walk at all," she said in a social media video at the time.

Ray later documented her recovery, and videos showed her walking again before she became pregnant.

Born in Australia, Ray was the daughter of big-wave surfing legend Tony Ray. She grew up between Victoria, California and Hawaii.

She launched her swimwear brand, Sahara Ray Swim, in 2014 and amassed nearly 900,000 Instagram followers.

She first drew widespread attention in 2016 when she was photographed vacationing in Hawaii with Justin Bieber. The two were seen skinny-dipping.

Sahara Ray and Justin Bieber in a pool. Photo credit: Sahara Ray / Instagram

She was also linked to Australian singer Cody Simpson and, more recently, to musician Mod Sun.

Tributes began appearing on social media on Monday. Model Becky Hearn posted several photos of Ray on her Instagram stories, writing:

"Will always love you my sweet soul."

Ray's final Instagram post, from July, remains online. It shows Snow in a series of photos.

The caption reads:

"My whole heart baby Snow."

View this post on Instagram

A post shared by Sahara Ray (@sahara_ray)

Tesla Tells Shareholders Optimus V3 Robot Is Ready for Mass Production

2026-10-19T12:20:28.880-04:00

Tesla has told shareholders that its Optimus V3 humanoid robot is prepared for mass production, with production lines being developed, according to a company letter sent Monday and highlighted in an SEC filing. The letter, which also confirmed Tesla’s annual meeting for December 3 at Gigafactory Texas, listed Optimus among the company’s 2026 achievements.

Credit: Tesla SEC filing screenshot obtained by AllBlogThings

The shareholder update and SEC filing carried a message from CEO Elon Musk:

“The most likely outcome is an age of Amazing Abundance, where anyone can have anything they can think of... This is a message of optimism and excitement about the future”.

Tesla has strengthened its public claim about the readiness of its Optimus V3 humanoid robot, describing it as “Readied V3 Optimus for mass-production” in its 2026 shareholder materials filed with the US Securities and Exchange Commission on Monday, October 19.

Previous company guidance has pointed to output beginning before the end of 2026 at Tesla’s Fremont factory, with eventual planned capacity of 1 million robots per year.

The new language follows an October 12 disclosure by Hangzhou-based Xinjian Transmission, a Chinese components supplier linked to the Optimus programme. In a response to questions from the Shenzhen Stock Exchange about its planned IPO, the company said the robot’s hardware design was nearing finalisation, and the project was approaching mass production, according to Chinese financial reporting.

The letter from the company did not provide a production count or a revised timetable, and it also failed to specify a production start date.

However, the letter signed by board chair Robyn Denholm says Tesla continued developing its products and investing in the infrastructure needed to manufacture them at scale, including “the first production lines for Optimus.”

Credit: Tesla SEC filing screenshot obtained by AllBlogThings

Musk said on an April earnings call that production would likely begin around late July or August, after the Model S and Model X lines at Fremont are dismantled and rebuilt for Optimus.

He cautioned that initial volumes would be “quite low” because the robot has more than 10,000 distinct parts, each of which must enter mass production.

The machines currently coming off the line are not customer units. They are used for internal testing, training and related work, according to reporting from The Information. Production has climbed from a few dozen units a week in the second quarter to several hundred per week in August, but the robot’s hands remain a bottleneck: each hand and forearm contains more than 100 screws and other tiny parts.

Tesla has not confirmed any commercial sales timeline in the shareholder letter.

JPMorgan has said commercial sales are scheduled for the second half of 2027, and Oppenheimer has said Optimus revenue is unlikely before 2029.

The shareholder letter’s language, “prepared for mass production, with production lines being developed,” stops short of saying production has started.

Tesla did not respond to our request for comment on when the first customer units would ship. Grok did respond to our question, and it said:

The gap between “prepared” and “producing” matters for investors who have priced Optimus as a future catalyst.

Tesla’s automotive business is shrinking: fourth-quarter deliveries fell 15% year over year, and full-year deliveries dropped 8%.

The company has guided $20 billion or more in 2026 capital expenditure, a bet that includes robot lines, AI training infrastructure and battery production.

Elon Musk has called Optimus potentially “the biggest product ever”.

Until Tesla discloses customer deliveries, the mass production claim rests on installed lines and internal trials, not revenue.

News source: sec.gov/Archives/edgar/data/1318605/000110465926116718/tm2626016d2_ars.pdf

Kylie Jenner Wears $50 Million Victoria Secret Bra

2026-10-19T10:37:41.082-04:00

The 29-year-old reality star and beauty mogul "Kylie Jenner" became the first non-Angel to wear the jewelled piece, which returned after an eight-year absence and drew both praise and criticism.

Credit: Kylie Jenner / Instagram

Kylie Jenner made her Victoria's Secret Fashion Show debut on Sunday at Sony Pictures Studios in Culver City, California, wearing the brand's Fantasy Bra, valued at $50 million. It is the most expensive Fantasy Bra ever made and the first to appear on the runway since 2018.

The "Most Expensive Victoria's Secret Bra" contains 4,410 natural diamonds weighing 1,373.39 carats in total, set in 18-carat gold, with a 75.54-carat Fancy Vivid yellow diamond at the centre. Victoria's Secret said the centre stone alone is worth $22 million.

The piece has no fabric at all. Fifteen craftspeople at Jacob & Co. spent more than 1,000 hours building it, and it weighs just under four pounds.

Kylie Jenner wore Victoria's Secret Fantasy Bra with a white mini skirt, clear heels and white feathered wings. Her mother, Kris Jenner, and boyfriend Timothée Chalamet watched from the front row.

Kris Jenner even shared a heartfelt Instagram post:

View this post on Instagram

A post shared by Kris Jenner (@krisjenner)

Adam Selman, Victoria's Secret's chief creative officer, told Vogue that Jenner was chosen because she is a "cultural powerhouse." "Kylie really extends beyond fashion, beyond culture, beyond TV, beyond one singular thing," he said.

"The show is so big, it's true entertainment, and I think that she is a great catalyst for that." Selman said the "big idea for the show was fandomonium" and that the Fantasy Bra was "one of the things that was requested the most from last year's show."

Jenner is the first non-Angel to wear the Fantasy Bra. Previous wearers include Tyra Banks, Gisele Bündchen, Heidi Klum and Adriana Lima. The choice drew criticism from fans who said the honour should have gone to a veteran model.

The BBC reported that some commenters called it "pure disrespect to real models" and said the show had gone "from iconic glamour to an influencer circus."

Credit: Kylie Jenner / Instagram

Summer Nesbeth, a British model who has walked at New York Fashion Week, told BBC Newsbeat the selection was an attempt to keep up with young fans. "She's an influencer with a lot of power and we are in the social media age," Nesbeth said.

Joel Marlinarson, a social media and marketing expert, told Newsbeat that brands now prioritise "who's going to bring the most eyeballs" over "who fits the cookie cutter definition of a model."

He said younger viewers connect with Jenner, while older fans who remember "the iconic Naomi Campbell era" may see the choice as a "betrayal."

Kylie Jenner in Fantasy Bra. Credit: Kylie Jenner / Instagram

Victoria's Secret scrapped its fashion show after the 2018 edition drew low ratings and criticism that it was sexist and lacked diversity. The show returned on Sunday.

Switch Models Freely: One Workspace, Many Text-to-Video Engines

2026-10-19T08:20:00.000-04:00

Video projects vary in how creative they need to be, ranging from a quick idea draft to a cinematic scene or a stylized animation. Some projects focus on motion, others on steady characters, on the right reference, or on certain image styles. One generation approach is not always enough when project requirements change, as it reduces experimentation. Dreamina AI is a platform that integrates several creative tools and video generation models. This setting allows you to experiment with various interpretations, tweak successful shots, and work creatively without having to recreate the workflow. Knowing about the distinction between models will enable you to choose the outputs based on the actual needs of the project.

Understand What a Multi-Model Text-to-Video Workspace Offers

A multi-model workspace is a creative environment that offers access to various generation engines. Prompts can be interpreted differently by each engine and influence motion, composition, lighting, and visual details. Dreamina AI has the ability to support the Seedance family and other available models, depending on the interface. It has a text to video workflow that can help create video scenes based on written descriptions without any prior footage. Compare interpretations, modify instructions, and find creative solutions, all in one platform. But different models may have different capabilities, generation times, durations, resolutions, and editing controls. Don't just assume that the newer model is always the better choice; look at the output to see if it meets your needs.

Choose Models According to the Creative Problem

Choose a model based on the challenge your project is to address. In the early stages of concept development, it can be helpful to freely create and rapidly experiment to explore various visual directions. For complex scenes, more detailed analysis of movement, interaction of objects, and continuity of characters is needed. The image or video inputs that are supported in reference-guided projects provide a visual intention to the project. For longer projects, it is important to check the duration limits available before planning out full sequences. Before committing resources, consider aspect ratios, resolution settings, editing controls, and generation costs. These are used as guidelines for selecting the model, but no specific version is guaranteed to have a capability. When every experiment is linked to a well-defined creative goal, Dreamina AI makes comparison relevant.

How to switch between engines with Dreamina's AI text-to-video maker

Step 1: Enter the workspace and prepare a test prompt

Sign up or log in to Dreamina to open one workspace for every text-to-video engine.
Go to the "Create" tab from the left menu and select the "AI video" option.
Write a detailed text prompt you can reuse across engines and compare the outputs later. Include video elements, sound, background, theme, and concept so every engine gets the same brief.

Step 2: Pick an engine and generate

Choose your first model from the options present at the bottom of the dialogue box.
Try models such as Dreamina Seedance 2.5, Dreamina Seedance 2.0, Dreamina Seedance 2.0 Fast, Dreamina Seedance 2.0 Mini, Dreamina Seedance 1.0, etc.
Click on "Omni reference" to guide the AI with multiple reference images for consistent results.
Next, select the custom aspect ratio and resolution from the "Aspect ratio" and "Resolution" tabs.
Finally, click on the "Generate" tab to begin creation, then swap models and repeat it.

Step 3: Compare results and export

1. If a result misses the mark, you can "Edit prompt" or "Regenerate" with another model.

2. You can also edit the generated video using the "Edit video" option for small fixes.

3. Moreover, you can use "Edit segment" to edit only one part of the chosen video.

4. Use "Extend video" to lengthen your favorite result, or try "Creative upscale" for sharper quality.

5. Dreamina gives you complete freedom to mix engines and edit the video your own way.

6. Finally, click on the "Download" tab present at the top right of the interface. This saves the video file on your local device so you can compare engines later.

Six Factors for Comparing Text-to-Video Model Outputs

Prompt interpretation: Consider if it fits the subject, setting, action, and visual direction you are going for.
Movement: Check for discrepant or visible incongruities in movement, interaction of objects, and space.
Visual Consistency: Look at the overall consistency in terms of character, background, lighting, and key details across the series.
Reference alignment: Check generated visuals with provided references and composition.
Creative style: Assess if the style, mood, colour, framing, and visual language are appropriate for your brief.
Workflow suitability: Take into account generation time, editing options, output settings, credit costs, and refinement requirements.

This scheme is useful in objectively comparing useful results rather than universally ranking models. Even a visually impressive clip can be unsuccessful if it lacks critical information or needs a lot of editing.

Run Meaningful Model Experiments Without Wasting Generations

Use a single, well-worded prompt and the same settings as much as possible in the platform. Note the chosen model, wording, inputs, resolution, and generation mode. Vary one significant factor at a time to see the effect of the change. Try a short representative scene first, before trying out a complex sequence. Don't blame a difference on a model if the prompts or settings were different. Be sure to compare credit fees, as consumption could differ based on model, length of time, resolution, and function. Keep and record good results and note their drawbacks. While Dreamina AI offers a collaborative space for iterations, it is crucial to make careful comparisons. Perfect the best candidate before venturing into other creative avenues that may not be related to the project.

Combine Model Flexibility With References and Targeted Editing

For ideas that are still "exploratory," a text-only prompt is appropriate, and creative variation is still acceptable. Where available, Dreamina AI's Omni reference enables several images to be referenced, aiding visual direction. Movement, camera behavior, or scene relationships may be further guided by supported video references. But references cannot assure exact reproduction or continuity. Review footage for visual drift and for unanticipated detail or actions. Make prompt adjustments or regeneration where the overall interpretation requires improvement. When supported, edit segment can be used to work on a particular part; external editing provides more control with regard to precise timing and transitions. Model switching, references, and localized editing are different issues; use each one with care.

Conclusion

Dreamina AI provides a flexible workspace to experiment with video generation models, test out the visual interpretations, and improve selected video footage. Its worth is determined by the combination of available capabilities and creative requirements of each of its projects. Always start with a clear goal, compare under the same conditions, and check the model settings before you generate. Check for movement, reference alignment, visual consistency, and refinement expenses prior to picking your end outcome. A thoughtful workflow is based on evidence and suitability rather than model rankings, which can guide you to make informed creative decisions for a variety of video projects.

OpenAI image_gen Tool Showing an Empty Message? Here's How to Fix It

2026-10-19T05:13:56.021-04:00

You hit generate, watch the spinner do its little dance, and then… a message with no image. No error code. No helpful explanation. Just an empty response where an AI-generated picture should be.

I have seen this exact frustration fill OpenAI Community threads and GitHub issue trackers for months, and the cause is rarely what you think it is. I have faced this problem in my ChatGPT image generation responses too and have found the fixes.

The good news is that most empty-message cases trace back to one of four specific problems, and three of them you can fix in under five minutes. The fourth one is not your fault at all.

Why the image_gen Tool Returns an Empty Message

The image_gen tool is not a single program running on your phone or browser. It is a chain of handoffs between your device, OpenAI's chat model, and a separate image generation service being offered for free in ChatGPT. When any link in that chain breaks, the image never arrives, and the chat interface often shows nothing instead of a useful error.

Between July 21 and October 14, 2026, the company logged 12 separate image-related incidents, nine of which affected ChatGPT's Image Generation component specifically. Some of those incidents produced explicit error banners. Others produced the silent empty message as the one you can see below:

Four failure patterns account for nearly every empty-response report:

Server-side incidents where the image service is degraded but the chat interface stays online.
Session and browser interference from cached data, VPNs, or aggressive extensions.
Prompt and policy blocks that the chat model does not translate into a readable error.
Tool exposure bugs in the desktop app and Codex builds that remove image_gen from the available tool list entirely.

The fix changes depending on which pattern you are in. That is why clearing your cache sometimes works instantly and other times does nothing at all.

First, Check If the Image Already Exists

Before you touch a single setting, open the Images section in ChatGPT's sidebar. This is the single most skipped step in image generation troubleshooting, and it resolves a surprising number of cases. OpenAI saves every image you create with ChatGPT to the library automatically, even when the chat interface fails to display it.

I did this many times, and surprisingly, this works on Chrome.

During a July 2026 incident, users saw "Image generation failed" in the chat while the finished image was already sitting in My Images. The failure appeared in Chrome, Firefox, Edge, and Safari, but the image existed on some accounts and not others.

Like me, if you find the image there, download it and move on. You do not need to troubleshoot anything.

Match the Message to the Fix

The words on your screen tell you which failure pattern you are dealing with. Read them before you do anything else, because logging out and clearing your cache will never fix a plan limit or a policy refusal.

What you see	What it usually means	What to do
Status page shows a problem on "Image Generation"	OpenAI incident	Save your prompt and wait for the incident to resolve
"Image generation failed. Try again." or a long hang on "One last tweak"	The image step failed or stalled	Check Images in the sidebar first, then retry once in a new chat
"I wasn't able to generate the image due to an error on my side"	No official explanation; most likely the image tool failed	Same as the row above
A banner with a reset time (for example, "720 hours")	You have hit your plan's image limit	Note the reset time and wait; retrying will not shorten it
"May violate our guardrails"	Post-generation policy check	Edit the part of the request that matches the named category
A custom GPT answers only in text	Image Generation is turned off in that GPT	Ask the builder to enable it, or use regular ChatGPT

The "I wasn't able to generate the image due to an error on my side" message is worth a closer look. It reads like a polite apology, but it is actually the chat model telling you that its call to the image tool failed. It does not come with a reset time like a limit banner, and it does not name a policy category like a refusal. Treat it exactly like "Image generation failed" and work through the browser and session fixes below.

The empty-message problem behaves differently depending on where you are using image_gen. A fix that works on the web can be irrelevant on the desktop app, and the API has its own separate failure modes.

ChatGPT on the Web

Web users hit empty responses most often during peak demand while OpenAI's infrastructure takes a beating roughly between 10 a.m. and 2 p.m. and again from 7 p.m. to 11 p.m. Eastern, and image requests are usually the first thing to stall.

Start by loading status.openai.com and looking for the component labeled "Image Generation," not just the overall ChatGPT line. The API has its own separate component called "Images," and a problem on "Images" alone affects developers using the API, not necessarily the ChatGPT app. If the status page is green but your failure matches a wave of same-day reports from other users, treat it as an incident anyway and wait a few hours before trying account-level fixes.

If the status page is clean and the Images library is empty, move to the browser and session fixes in the next section.

ChatGPT Desktop App and Codex

The desktop app has a distinct failure mode that produces a truly empty response: the built-in image_gen tool sometimes disappears from the session entirely after an auto-update. The imagegen skill is still installed and can be loaded, but the session does not expose the tool, and the app redirects you toward a CLI fallback that asks for a separately configured API key.

This is a tool provisioning regression, not a quota or account problem. Users on both macOS and Windows have reproduced it across multiple builds. In one controlled rollback test, the same account and model that received the built-in image_gen tool on an older Codex CLI immediately lost it after the desktop app updated to a newer bundled CLI. Reinstalling the desktop app does not help because it installs the same broken build.

A related bug involves stale cached plan data. Even on a ChatGPT Pro account, the app's cached subscription fields can report chatgpt_plan_type=free, which suppresses the image tool without showing an error. If you are on the desktop app and the tool itself seems to be missing rather than failing, check for a newer build or revert to a previous version if your workflow depends on built-in image generation.

API and Third-Party Clients

API users see empty responses for a different set of reasons. An empty response body from the Images endpoint usually means the request reached OpenAI but the response did not contain the expected data[0] field. This can happen when the model returns a refusal or text item instead of an image, when the response format is set to URL but your code expects base64, or when a proxy or third-party client alters the response before it reaches you.

Log the full response body and inspect the actual top-level keys. If the output array contains a text or refusal item instead of an image_generation_call item with a truthy result, the request was processed but the image step declined. Reword the brief to avoid policy-sensitive content and verify that your tool configuration matches the current OpenAI documentation.

Third-party clients add another layer. LibreChat, for example, has historically interpreted DALL-E's base64 image output as text, pruned the long string, and produced an error instead of an image. If you are using a wrapper or self-hosted interface, test the same prompt directly against the OpenAI API before blaming the model.

The Browser and Session Fixes That Actually Work

When the status page is clean and the image is not already in your library, these steps resolve the majority of empty-response cases on the web. Work through them in order and stop as soon as one works.

Start a fresh chat. Long conversations sometimes cause image requests to stall. OpenAI recommends a new conversation when responses become slow or stop generating altogether. Paste your prompt into a brand-new chat and send it there.
Retry once with a shorter prompt. Very long prompts are a known failure trigger. One heavy user found that prompts of roughly 2,000 to 7,000 words failed while a prompt of about 400 words usually worked. Another found that images failed in the desktop app with "High" thinking but worked on the website. Strip your prompt down to the core subject and composition, then add details back one at a time.
Open an incognito or private window. This isolates your session from cached files and extensions in one step. OpenAI's support staff specifically suggested an incognito window, clearing browser cache and cookies, and testing another browser after the July outage was resolved.
Disable VPNs, ad blockers, and privacy extensions. VPNs, aggressive ad blockers, and secure DNS services can interrupt image generation requests before they reach OpenAI's servers. Turn off your VPN, disable browser extensions, and pause secure DNS or web filtering, then test in Incognito Mode.
Clear cache and cookies for ChatGPT and OpenAI specifically. You do not need to wipe your entire browser history. Every major browser lets you clear data for specific sites. Search for ChatGPT and OpenAI in your browser's site data settings and delete only those entries.
Log out and sign back in. An expired authentication token can cause image requests to fail silently. Logging out, closing the app or browser, and signing back in refreshes your token and reconnects your account to OpenAI's servers.
Try generating from the Images page instead of a chat. Go to chatgpt.com/images and start the generation there. During a July 2026 incident, one user reported that this page worked when the same request failed inside a chat that used thinking mode.

If you are on a phone, test the web version (or the reverse). That tells you whether the problem lives in the app or in your account.

When to Stop Troubleshooting and Contact Support

There is a point where more retries stop being productive, and you end up getting the same error message.

If the status page has marked an incident as resolved, you have worked through the browser and session steps, and the empty message still appears, OpenAI's support staff treat it as a possible account-specific problem.

You now have to send a message to OpenAI's email at support@openai.com with screenshots and the case number from any previous interaction.

Do not keep generating if you see an account or country block message. That is not a bug you can clear with a cache wipe. Contact support and wait for a response from them.

The empty message is frustrating precisely because it gives you nothing to work with. Once you know which layer of the chain has failed, it stops being a mystery and becomes a checklist.

Make sure, check the library, check the status page, match the message to the pattern, and work the fixes in order. You will either have your image back in a few minutes, or you will know exactly why you do not.

How Artists Can Build an Online Presence That Travels Beyond Social Media

2026-10-18T17:18:00.000-04:00

Social media can help people discover an artist, but a following on one platform is only part of a durable online presence. A useful website, thoughtful articles and relationships with relevant publications can help your work reach people who are already interested in art. The goal is not to publish everywhere. It is to make it easy for the right audience to find your work, understand it and take a next step.

Give your work a reliable home

An artist’s website should answer a few practical questions quickly: who made the work, what kind of work is it, where can someone see or buy it, and how can they get in touch? A clear portfolio, short biography and current contact details are often more valuable than elaborate effects or a complicated menu.

Organise images so visitors can browse without guessing. If you work across several mediums or series, group pieces into useful collections and give each image an informative caption. Include dimensions, materials and dates where relevant. Keep the site usable on a phone, since a visitor may arrive from a social post or an online article while away from a computer.

It also helps to decide what you want a site visit to lead to. A gallery might want curators to view its artists; a maker might want visitors to enquire about commissions; an art business may be looking for wholesale customers. Put the appropriate next step somewhere visible, without turning every page into a sales pitch.

Use guest articles to reach interested readers

Writing for another publication can introduce your work to readers beyond your existing audience. A useful guest article offers something of value in its own right: a look at a process, an explanation of a technique, a perspective on a local art scene, or practical advice for people learning to make or collect art. The artwork can appear as part of the story, rather than being the story’s only reason to exist.

Before pitching, read the publication and identify the kind of article its readers respond to. A tutorial may suit a site serving beginners, while a discussion of conservation or exhibition planning could fit a more specialised readership. Match the pitch to the publication’s subject and style, and propose a specific angle instead of sending a general offer to write about art.

Artists looking for possible outlets can browse collections of art guest posting sites as a starting point. Check each publication’s current guidelines and audience before investing time in a pitch. A list can help with discovery, but relevance matters more than the number of places contacted. A small number of well-matched contributions is usually more useful than a scattershot campaign.

Make the article genuinely helpful, follow the editor’s requirements and use a brief author biography to explain who you are. If the publication allows a link to your portfolio, it should take readers to a page that continues the topic or shows the work discussed. That gives an interested reader a natural way to learn more without interrupting the article.

Plan a website before hiring help

Not every artist needs to build a website alone. A freelance designer or developer can help with layout, accessibility, shop features or technical maintenance. But even a skilled professional needs a clear account of what you want. Requests such as “make it modern” or “I need a simple art website” leave important questions unanswered, creating room for different assumptions about the scope and result.

To avoid vague website project requests, write down the site’s purpose, intended visitors and essential actions. List the pages you need, such as a portfolio, biography, events page or shop, and describe what each page must contain. Share examples of visual styles you like, noting what specifically appeals to you—perhaps the typography, spacing or image layout—rather than asking someone to copy another site.

Include practical details too: whether you already have a domain, images and written copy; whether you need a content management system; and who will update the site after launch. State any deadlines, budget limits and technical requirements you know about. Separate must-have features from ideas that can wait. This makes it easier for a freelancer to estimate the work and identify questions before the project begins.

A guide on writing a web developer brief can help turn those notes into a clearer plan. Osdire, a freelance marketplace with services across areas such as programming, design and marketing, is one place where buyers can find independent help. Whichever route you use, agree on deliverables, milestones and how feedback will be handled before work starts. Clear expectations protect both sides and reduce the chance of costly revisions.

Keep the presence active, not frantic

An online presence does not need constant updates to be useful. Set a manageable rhythm: refresh your portfolio when a new body of work is ready, add dates for upcoming exhibitions, and publish an article when you have a worthwhile idea. Check that contact details and links still work, and remove information that has become misleading or outdated.

Finally, notice which efforts lead to meaningful responses. A guest article may bring thoughtful enquiries, while a social post may earn quick attention but little follow-through. Use those observations to choose where to spend your time. A clear website and a few well-placed contributions create a stronger foundation than trying to keep every platform busy.

Baby Goes Viral For His Extreme Relaxation Minutes After Being Born (video)

2026-10-17T14:20:05.266-04:00

A clip of a baby lying with his hands behind his head and legs crossed has millions of views. Initial media reports disagree on which Egyptian doctor filmed it, and the family of the baby has not been identified yet.

A newborn lying with both hands behind his head and one leg crossed over the other has become the subject of a viral clip posted on X with the caption: "Baby goes viral for his extreme relaxation minutes after being born." The post, first shared by the account @crave_indie on 16 October, had more than 2.4 million views by the next day, according to X's public counter.

Watch the video here:

The footage shows the baby on a blue medical drape, one leg crossed over the other and one hand tucked behind his head, while a doctor in scrubs examined him with a stethoscope. The baby's name, the hospital and the date of birth were not included in the post.

Earlier reports of a newborn in the same pose appeared in September. Crescer, a Brazilian outlet published by Globo, reported that the video was recorded by Egyptian pediatrician and neonatologist Mohamed Ali Atia in a hospital in Egypt.

Briefly News quoted his Instagram caption:

"The mayor of Al-Hassayna has arrived. This is not a mayor. This is a foreign minister."

Alô Alô Bahia, another Brazilian outlet, reported that Atia called the baby "mayor of Al-Hassaniya," a reference to a locality in Dakahlia province, Egypt.

It said the first video had passed 37 million likes on Instagram and a second had passed 22 million views.

We learned from the original video that the doctor in the footage was Islam Eldeh, an Egyptian neonatologist, and that he smiled and asked the baby, "What are you doing?" before checking the baby's heartbeat with a stethoscope.

Islam Eldeh checking the viral baby (newborn)

NDTV has previously identified Eldeh as an Egyptian neonatologist who posts videos of newborns.

Neither Atia nor Eldeh has issued a statement that resolves the discrepancy. Alô Alô Bahia reported that Atia did not reveal the baby's name, the hospital or the exact date of birth, and that it was not clear who filmed the video. The October X post did not name a doctor or a hospital at all.

The caption describes the baby's manner as "extreme relaxation." Newborns often keep the flexed posture they held in the womb, and many early movements are involuntary reflexes, according to Mi Bebé y Yo, a Spanish parenting publication.

A crossed leg and a hand behind the head can look like a holiday pose.

The spread of the clip has produced a familiar split between the image and the record. The image is easy to share: a newborn who looks as if he has already checked into a resort. The record requires a name, a place and a date, and those are the details that have not travelled with the clip.

No one has commented on behalf of the now viral baby.