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OpenAI has formally outlined a national science initiative designed to connect frontier AI models with government research infrastructure, National Laboratories, universities, and working scientists. The program is not a single model launch. Instead, it combines funded access, early product access, scientific campaigns, and an emphasis on fitting advanced AI into real research workflows. The initiative gives concrete form to OpenAI’s stated goal of helping scientists use increasingly capable models to accelerate discovery. In its official announcement on advancing the next era of national science , published July 22, 2026, the company describes a long-term strategy built around the U.S. Department of Energy’s Genesis Mission and collaborations with National Laboratories. The core proposition is that AI can contribute to hypothesis testing, simulations, and experimental work when it is deployed alongside scientific infrastructure and human expertise. That framing matters. OpenAI is positioning frontier models as tools that researchers direct and evaluate, rather than as a replacement for the institutions and specialists responsible for scientific work. What OpenAI is providing to scientific researchers OpenAI’s commitments span several types of access, from coding support for a broad research community to model capabilities and API funding for large campaigns. The announced provisions include: $4 million in Codex access for approximately 2,000 Genesis researchers at national labs and universities. $3 million in API support for two large scientific campaigns. Up to $10 million in API usage for participating researchers who reach a $2.5 million spending threshold. Access to GPT-Rosalind’s bioscience capabilities for national-lab researchers. Early access to selected models and features for trusted national-lab leaders preparing workflows and evaluations. Expanded access to advanced cyber capabilities for national-lab cybersecurity researchers. These commitments indicat

I just made Whizz, an esoteric programming language that is full of capability and possible experimentation. Before I interest you in that, I'll explain to you something. What is an esoteric programming language? An esoteric language (or an esolang, colloquially), is a programming language designed to not fit the coding 'norms' or conventions. Take an example: BF ('BF' is an abbreviation and euphemism of brainf***). A standard language would notate a 'Hello, World!' program as something like: print ( " Hello, World! " ) BF, on the other hand, requires something like this: ++++++++ [ > ++++ [ > ++ > +++ > +++ > + <<<< - ] > + > + > - >> + [ < ] < - ] >> . > ---.+++++++..+++. >> . < -. < .+++.------.--------. >> +. > ++. As you can see, BF, like most esolangs, is different: it's hard to write and a puzzle. Whizz is inspired by BF, as its incrementing, decrementing and looping are inspired by it. I made Whizz because I thought languages like BF were way too monotonous to write. Esolangs should be hard and puzzling to write, but not laborious. BF requires you type '+' as many times you want to increment (without loops): so you have to find shortcuts and unscalable solutions, just to achieve your goal. In Whizz, just type that incrementation repetition count before the '+' sign, and there you have it! These wonderful features that Whizz boasts keep the challenge in esolang-ing, but contradictorily makes it more 'scalable'. Another notable feature is functions: the epitome of order. An example of a Whizz program would be: zeroToNine { [ create variables ] counter 10+ [ track state ] char 48+ [ print this one ] space 32+ [ space char ] ( char!+ [ print and increment char ] counter-; [ decrement counter and end if zero ] space! [ print space ] ) } zeroToNine* This, self explanatorily, outputs '0 1 2 3 4 5 6 7 8 9'. Again, in minimized form: c10+n48+s32+(n!+c-;s!) I genuinely hope you experiment with Whizz, and solve puzzles & challenges with it, as if it were BF! Install it

If you just want the recommendation: for the ordinary AI-generated image an inference job hands back — a 2 to 8 MB PNG — do one plain object PUT into your S3-compatible storage and stop there, because multipart upload only earns its complexity when a single artifact is big enough that losing a transfer halfway through costs you real money to redo, which for my team starts somewhere north of 100 MB. Everything below is about that threshold, and about the operations bill you pick up the moment you cross it. I run the platform roadmap for a team that renders a few hundred thousand images a month, and I count pages before I count features, so read the rest with that bias in mind. Should I use multipart upload for large AI-generated images, or a single object PUT? Multipart solves two narrow problems: a payload too awkward for one HTTP round trip, and a transfer you refuse to restart from byte zero. A 6 MB PNG has neither problem. The shape of the flow is always the same wherever you run it. You start a multipart upload and get back an upload id, you push each part under that id, you collect the returned ETag and part number for every one of them, and you send the finished list back in a complete call that stitches the object together server-side. Parts have to be at least 5 MiB on Amazon S3 and on every S3-compatible store I've tested against, with the final part exempt, which already tells you the feature was designed for objects measured in hundreds of megabytes rather than for a batch of thumbnails. Where it genuinely pays off in an image pipeline is the long tail: a 4-gigapixel tiled upscale, a nightly ZIP export of a customer's whole render history, a raw latent archive somebody in research wants kept for a year. Those are the jobs where a dropped connection at 80% is a real incident and not a shrug. For everything else, one put is one line of code and one thing to monitor. There's a second cost that people underrate, and it's the one I'd argue about in a design re

I went looking for a small improvement to an open-source tool. I found a number that pointed the wrong way, and then I found out why it had to. Live demo — paste your own file and watch it happen: https://pin-on-expand.onrender.com The setup Paritok is a 4B model that compresses AI coding-agent context. It sits between your agent and Anthropic or OpenAI, squeezes the file reads and tool output, and tells you what it saved. It's genuinely good work. Trained on 45,000 real agent trajectories, so it knows a function signature matters more than a debug line. Apache 2.0. Runs on a consumer GPU. Their benchmark numbers hold up. I wanted to build a policy improvement on top of it. To prove my improvement helped, I first had to measure what stock Paritok cost. That measurement is the whole story. Two numbers that disagree One coding-agent session. One 20,005-token file in context. Paritok's own /stats endpoint: 64.0% of input tokens saved. What the provider was actually POSTed: 69.2% more than sending the file with no compression at all. Same session. Same file. Both numbers correct. Where the missing tokens went Paritok is non-destructive by design, which is the good part. Compressed content gets tagged [REF:id] , and when the model needs the exact original it calls an injected expand_context tool to pull it back. Lossy on the wire, recoverable when it counts. The proxy answers that call itself . It appends the full original to a proxy-local thread and POSTs that thread upstream a second time. And stats is computed once, in process_request — before that loop runs. post 0: 6,919 tokens compressed request ← counted by /stats post 1: 26,924 tokens carries the full original ← never counted ───────── billed: 33,843 Then it compounds. The proxy conceals the virtual exchange from the client, so your agent never sees it. Next turn the agent re-sends the original file, Paritok re-compresses it to the same reference, and the model expands it again. Every turn. Forever. In fairness:

OpenAI has published a post-mortem examining an unusual pattern in its model testing: recurring references to “goblins” and “gremlins” in model outputs. The company’s official post, “Where the goblins came from” , published on April 29, 2026, frames the behavior as an emergent effect of reinforcement learning and human-feedback dynamics, not as a new product feature. Its practical message is more consequential than the metaphor suggests: unexpected model personas can affect the consistency, safety, and reliability that developers expect from AI systems. The published analysis provides the substantive context behind recent attention to a purported “goblin-level” post. Rather than indicating a model launch, OpenAI’s account suggests a narrower but important lesson about how optimization signals can inadvertently reinforce patterns in language models. For organizations using LLMs in production, the relevant question is not whether goblin-like language is amusing. It is whether teams can detect and address unexpected behaviors before those behaviors influence customer-facing, operational, or high-stakes workflows. What OpenAI documented OpenAI said the “goblin” and “gremlin” metaphors appeared during GPT-5.x testing and RLHF training. The company reported a notable increase in goblin-like language during GPT-5.5 testing when Codex was being evaluated. According to the post, the pattern emerged from reward-signal dynamics : persona-like responses were inadvertently reinforced through reinforcement learning and human feedback. That distinction matters. OpenAI does not characterize goblin behavior as a fixed capability or intentional model identity. It describes it as a byproduct that can arise at scale when a training and feedback process favors certain output patterns. The episode is therefore best understood as an alignment and evaluation lesson, rather than evidence of a separate “goblin” model, feature, or policy release. OpenAI also described a mitigation introduced

A memo obtained by WIRED, issued by the water utilities information sharing group WaterISAC, links dozens of cyberattacks against Minnesota water utilities to Tehran.

What Is Temperature in AI? (And How to Stop Getting Poetry When You Asked for a Grocery List) Remember Magic 8-Balls? Those plastic oracles you'd shake for life advice, only to get "Reply hazy, try again" when you asked if your crush liked you back? Imagine someone added a little dial on the bottom. Turn it all the way to zero and the thing becomes painfully predictable, only ever offering "Yes" or "Most likely." Crank it all the way up and suddenly it's inventing answers that never appeared in the original twenty options, things like "Ask your neighbor's cat" and "The moon suggests Thursday." That dial is temperature, and every AI language model has one. How the dial works Temperature is a setting, usually ranging from 0 to 2, that tells an AI model how much risk to take when picking the next word. The model calculates the probability of every possible next word, then has to pick one. At low temperatures, it plays it safe and picks the most probable option almost every time. At high temperatures, it's willing to gamble on unlikely choices further down the list. This is why you can ask ChatGPT the exact same question twice and get a straightforward answer on Monday and what appears to be surrealist fiction on Tuesday. When you ask ChatGPT to write a professional email at temperature zero, you'll get "Dear Sir or Madam, I am writing to follow up on our previous correspondence..." every single time you hit enter. Set temperature to 1.5 and it might open with "Greetings, fellow traveler of the inbox wilderness" because that phrasing, while statistically improbable, is now in play. Why boring is sometimes good At temperature zero, you get the most boring dinner guest imaginable. It always picks the single most likely next token (the technical term for a chunk of text, usually a word or part of one). No variety, no surprises, just the statistical favorite every single time. This turns out to be perfect when you need factual accuracy, code that actually compiles, or data

There is a number going around that roughly half of all remote MCP servers are dead. I had repeated it myself, in the README of a tool I published. I could not find where it came from, so I measured it. The answer is that about one in ten is actually broken. The "half" figure appears to come from counting servers that require an API key as if they were down. Here is the method and the full breakdown. What I measured On 29 July 2026 I pulled every entry from the official MCP registry — 1,200 servers. Of those, 297 had status: active and advertised a remote endpoint URL (the rest are stdio/local packages with nothing to probe over the network). Each got one anonymous JSON-RPC initialize over streamable HTTP, with a 10 second timeout: { "jsonrpc" : "2.0" , "id" : 1 , "method" : "initialize" , "params" : { "protocolVersion" : "2025-06-18" , "capabilities" : {}, "clientInfo" : { "name" : "mcp-uptime" , "version" : "0.1.0" } } } Then I classified the response: a valid result containing protocolVersion or serverInfo is up, 401/403 is auth-gated, and everything else got bucketed by its actual failure. Results (n = 297) Result Count Share Completed an MCP handshake 133 44.8% Auth-gated (401/403) 134 45.1% DNS failure 8 2.7% Server error (5xx) 6 2.0% Not found (404/410) 5 1.7% Redirect (307/308) 4 1.3% Timeout 2 0.7% Non-MCP response 2 0.7% Other (400, 405, connection) 3 1.0% Reachable: 267 (89.9%). Genuinely broken: 30 (10.1%). Where "half are dead" comes from Look at the first two rows. 55.2% of these endpoints will not complete an anonymous handshake — and that is suspiciously close to the number people quote. But 134 of those 164 are returning a clean 401 or 403. They are running. They are answering. They want an API key, which is a completely reasonable thing for a hosted service to want. Counting those as dead inflates the failure rate by roughly five times. This matters beyond pedantry: if you believe half the ecosystem is rubble, you build defensively against the wron

Exploits can give persistent server access that survives credential rotation and disk re-imaging.

I am not by any means an expert at finance but I think I do now have some advice for people who are: Do not name your hedge fund anything that will be hilarious if it blows up. Don't use a name like "Long-Term Capital Management," or "Amaranth Advisors" (named for the floral symbol for […]

Satellites show burn scars and fires at AWS data centers and Saudi oil refinery.

A federal judge said the Trump administration has not presented enough evidence to justify labeling Anthropic a supply-chain risk, casting doubt on the government's ban on its AI technology.

Researchers fear AI is moving too fast, while Mark Zuckerberg is worried about who owns it. Plus: Inside Black Forest Labs’ push into robotics.

The last two versions of Chrome have included more patches than the previous 23 combined.

Hey DEV Community! 👋 I am currently building MOKSHA, an HTML5 Canvas game deeply rooted in Vedic philosophy. The game involves managing your Karma, avoiding Maya (Illusions), and achieving spiritual liberation. Ironically, while building a game about waking up from cosmic illusions, I fell into a technical illusion myself yesterday. Let me tell you a chaotic detective story about how my game froze on a fresh repository clone, and how I found the silent assassin hiding in plain sight. 🤡 🚫 The Disaster: Works on Itch.io, Freezes on GitHub So, there I was, ready to release a fresh update. I generated my build packages locally, zipped them up, and proudly uploaded them to Itch.io. I hit Publish, tested the live link, and everything worked flawlessly. High scores, smooth frames, total spiritual awakening. Then, I casually walked over to my terminal, ran git add . followed by git push, and went to bed thinking I was an absolute pro. The next morning, I wanted to double-check my clean repository, so I cloned it fresh into a new folder. I booted up the local server, and... the entire game was completely unclickable. Dead clicks. Frozen canvas. Total illusion (Maya). 💀 Opening up the browser console revealed a fierce wall of red text: style.min.css:1 Failed to load resource: the server responded with a status of 404 (Not Found) main.min.js:1 Failed to load resource: the server responded with a status of 404 (Not Found) 🕵️‍♂️ The Realization: It Wasn't Me, It Was My .gitignore! Initially, I blamed my sleep-deprived brain, thinking I forgot the chronological order of pushing and building. But when I opened my root directory to inspect the crime scene, I found the real culprit staring right back at me on lines 46 and 50 of my .gitignore file: dist/ *.zip index.min.html The Ultimate Trap Exposed 🪤 Because dist/ was explicitly blacklisted in my .gitignore, Git was literally doing its job perfectly by completely ignoring my production builds during staging! Here is exactly how the

As experts have warned for the last two years, some companies — like Microsoft and now Google — are finding and patching an exponential number of bugs in their products, thanks to the use of LLMs and AI tools.

Whether you are using off-the-shelf AI coding tools like Claude Code and Cursor or building custom autonomous AI agents with TypeScript and LLM APIs, you hit the exact same fundamental wall: AI agent amnesia . As an agent user , you spend forty-five minutes explaining your architecture, deployment quirks, and database rules. The agent writes brilliant code. You close the CLI or tab, open a new session the next morning, and the agent suggests the exact legacy library you rejected yesterday. As an agent builder , you struggle to keep your custom agentic loops focused. As multi-step agent trajectories expand, LLM token limits force context compaction, wiping out subtle rules and past decisions while escalating API costs. The intelligence is real. The amnesia is structural. Context Windows Are Working Memory, Not Long-Term Memory The AI industry’s standard reflex to agent amnesia has been pushing context windows to 1M+ tokens. But a context window is working memory (RAM), not long-term storage (disk). Relying on massive context windows introduces three critical engineering bottlenecks for both users and builders: Context Compaction Destroys Rationale : When a session reaches token limits, agents automatically compact their context history. Compaction summarizes conversations into short summaries, quietly wiping out subtle architectural constraints, edge cases, and past decisions. Context Drift & Attention Loss : LLMs struggle with needle-in-a-haystack attention degradation when context windows are stuffed with 100k+ lines of raw conversation history. Escalating API Costs & Latency : Re-sending full project transcripts on every prompt burns tokens rapidly and adds seconds of input processing delay for users while skyrocketing LLM bills for agent builders. Agents do not need larger transcripts. They need a durable, inspectable, versioned memory layer . Why Vector Databases Fall Short for Local & Workspace Agent Workflows When developers and AI engineers realize raw contex

If you're an African developer, SaaS founder, freelancer, or online business, one of the biggest challenges isn't finding customers. It's getting paid by them. Most articles about African payment APIs focus on moving money out of Africa. But what if your customers are the ones sending money to you? Whether you're billing international clients, collecting subscription payments, or accepting payments from marketplace users, you need a collection method that's easy for customers and simple to reconcile on your end. The Afriex Business API offers three different ways to collect payments, each designed for a different use case. Depending on who your customers are and how they prefer to pay, you can collect funds through dedicated virtual accounts, shared pool accounts, or stablecoin wallets. In this guide, you'll learn how each collection method works, when to use it, and how to integrate it into your application. The Three Collection Methods Although all three collection methods ultimately deposit funds into your Afriex Business wallet, they differ in how customers send money and how you identify who made each payment. Method Best for How the payer sends Dedicated virtual account Known customers, repeat payments Bank transfer to a unique account number Pool account Quick collection, one-off payments Bank transfer with a reference Crypto wallet Customers holding USDT or USDC Crypto transfer to a wallet address Choosing the right method depends on your product and your payment flow. If you already know your customers and expect them to pay repeatedly, dedicated virtual accounts provide the smoothest experience. If you want to launch quickly without creating individual accounts for every customer, pool accounts are a great fit. And if your users prefer paying with stablecoins, crypto wallets make that process straightforward. Method 1: Dedicated Virtual Accounts Dedicated virtual accounts are the easiest way to reconcile bank transfers from repeat customers. Instead of ask

Google Photos has launched Video Remix , an AI-powered editing feature that applies stylized templates to users' existing video clips. Powered by Gemini Omni , the tool is designed to turn a video into a more cinematic or artistic version through a one-tap workflow inside Google Photos. The feature matters because it brings generative video styling into a consumer photo library and editing workflow rather than requiring users to begin in a dedicated video-generation product. According to Google's official Video Remix announcement , templates can add cinematic relighting, replace backgrounds, and apply artistic treatments including watercolor, raw sketchbook, and oil painting. What Google Photos Video Remix changes Video Remix is built around easy-to-use templates rather than a conventional timeline editor. A user starts with their own clip, chooses a template in the Google Photos Create workflow, and receives a stylized result. That positions the feature as a fast option for personal memories, social posts, and short marketing assets where a full editing process may be disproportionate to the desired output. Google says Video Remix is beginning to roll out to eligible Google AI Plus, Pro, and Ultra subscribers in select countries. The supplied materials identify the subscription tiers, but do not provide feature-specific pricing or a complete country-by-country availability list. Access may therefore differ by market and rollout stage. Area Google Photos Video Remix Broader Gemini Omni context Primary workflow Applies templates to a user's existing video clips in Google Photos Supports wider video generation and editing workflows Documented examples Cinematic relighting, background changes, watercolor, raw sketchbook, and oil painting Style-driven video transformations, including claymation-style demonstrations reported by third parties Access described in supplied research Rolling out to eligible AI Plus, Pro, and Ultra subscribers in select countries Google docume

Florida wants to use federal EV charger funds to build an air taxi network connecting golf courses, luxury apartment buildings, and airports.