标签：#ai

If your first AI integration works in a demo but becomes slow, expensive, unreliable, or difficult to...

Not "AI-assisted." Not "copilot suggestions I edited." I mean I made a rule: for 30 days, I don't...

South Korea just did something no other country has done Let's start with the facts—I checked these one by one (sources cross-verified across multiple credible outlets, linked at the end): South Korea's Ministry of Science and ICT (roughly equivalent to a tech ministry) has finalized the three operating consortiums for its "AI for All" program: SK Telecom, KT, and Kakao. Beta starts in September, full launch by year-end. The core pitch is two words: free, unlimited . Doesn't matter what you earn, how old you are, or whether you're tech-savvy—all 52 million people in South Korea get direct access to generative AI, and officials are explicit: "no token limit." The government has allocated 512 NVIDIA B200 chips to these three companies this year, and starting in 2027, the national budget will even absorb part of the cost of running this nationwide service. That makes Korea the first G20 country to do this. (Quick reality check here: "unlimited" is the official claim, not physical reality. Feeding 52 million people off 512 chips necessarily means tiered rate limiting, or dumping most requests onto lightweight models—there's no way everyone gets truly unlimited access. Finding that hidden trapdoor is exactly what I plan to test myself later.) Every headline is chasing "free" and "unlimited." But free and unlimited is just the hook to get 52 million people flowing in—the real move that turns that traffic into a moat is buried in the next section. The real catch: that 80% There's a rule that every article glosses over, and it goes like this: Every operator must route at least 50% of queries to its own "certified Korean sovereign foundation model," and at least another 30% to other Korean companies' models. Combined, the floor for domestic models is 80% . I saw a viral social post claiming "at least 50% on domestic models"—that number is way too conservative. The actual floor is 80%. This is the soul of the entire program. "Free and unlimited" isn't charity—it's an incentiv

Every RAXXO tool ships under strict semantic versioning, major.minor.patch, and I never break that pattern even for a tiny fix A patch bump means nothing changed except a bug going away, a minor bump means something new showed up without breaking anything old, a major bump is a promise I rarely make The one time I skipped the discipline, a silent breaking change went out labeled as a patch and it cost me a support thread I could have avoided entirely The changelog and the version number are the same commitment written twice, and skipping either one breaks trust faster than any bug does Why a Solo Studio Even Needs This It would be easy to assume version numbers matter for teams, not for one person shipping small tools. I thought that too, before I had five live products and a support inbox that made it obvious how wrong that assumption was. A customer who bought a RAXXO tool eight months ago and only opens it twice a year has no idea what changed in between. The version number is the only honest answer I can give them without writing a personal message to every single user, and it has to be an answer that means the same thing every time. Semantic versioning gives me that consistency for free, as long as I actually follow it instead of treating it as decoration. The rule is simple to state: a patch release changes nothing except fixing something broken, a minor release adds capability without removing or altering anything that already worked, and a major release is allowed to break things, on purpose, with warning. The value is not in the rule itself, plenty of solo developers know the rule. The value is in never being tempted to shortcut it because a change feels small in the moment. I've written before about the changelog habit that keeps every RAXXO tool honest , and version numbers are the other half of that same commitment. A changelog entry without a version number attached to it is just a diary. A version number without a changelog entry explaining it is just

In the era of cloud-hosted AI, we’ve become comfortable sending our most sensitive data to remote servers. But when it comes to medical queries—like checking for drug-to-drug interactions —privacy isn't just a feature; it's a human right. 🛡️ With the recent explosion of WebGPU AI and the maturation of local LLMs , we can finally move the "brain" of our applications directly into the user's browser. In this tutorial, we are building a high-performance, browser-based AI tool that uses WebLLM and WebGPU to perform millisecond-level drug compatibility checks. No data ever leaves the device, ensuring 100% data residency and lightning-fast edge computing performance. The Architecture: Why WebGPU? Traditionally, running a Large Language Model (LLM) required a massive Python backend with expensive GPUs. WebGPU changes the game by providing low-level access to the local graphics card directly from the browser. WebLLM leverages this to run models like Llama-3 or Mistral in the browser sandbox. System Data Flow graph TD UserInput[User Inputs Medications] -->|React State| Engine[WebLLM Engine Instance] Engine -->|Compute Shaders| WebGPU[WebGPU API] WebGPU -->|Parallel Processing| LocalGPU[Device VRAM/GPU] LocalGPU -->|Token Generation| Engine Engine -->|Streamed Response| UI[React Frontend Display] subgraph Browser_Sandbox Engine WebGPU UI end subgraph Privacy_Boundary Browser_Sandbox end ExternalServer((Cloud / Internet)) -.->|Data Never Sent| Privacy_Boundary Prerequisites 🛠️ To follow this advanced guide, you'll need: Tech Stack : React (v18+), TypeScript, Vite. Library : @mlc-ai/web-llm . Hardware : A GPU supporting WebGPU (Latest Chrome/Edge/Arc). Step 1: Initializing the WebLLM Engine First, we need to create a singleton or a hook to manage our AI engine. Since loading a model (~2GB-5GB) takes time, we need to handle the progress state effectively. // useWebLLM.ts import { useState , useEffect } from " react " ; import * as webllm from " @mlc-ai/web-llm " ; export functio

I keep hammering the point that any coding-agent score is model + harness, not model alone. Same context-carryover rules, same note convention, same tool loop, same judge, or the comparison is garbage. Engrim (github.com/timgordontg/engrim) is a useful reminder that there's a third axis I've been underselling: state. It's a local-first SQLite memory engine for Claude Code, Cursor, Windsurf, Codex. Project-scoped, embeds records plus SQLite FTS, persists decisions and rationale between sessions. Hybrid retrieval, no cloud. Cross-session recall is the whole pitch. And here's the thing: that pitch only works if the agent actually gets better as its memory grows. Engrim exists because the authors believe persistent project context beats a cold context window on every new session. Which is exactly what almost no benchmark measures. Look at how agent evals are actually built. They load a repo, drop you in at an issue, and score the patch. Empty CLAUDE.md. No cursor rules. No memory file. Every run starts the model at the same amnesia. That's deliberate, for reproducibility. You can't run a score if the model's head is full of last run's secrets. But clean reproducibility bought a distorted measurement. The agent you benchmark cold is not the agent your team runs after a month of accumulated project memory. The more an agent keeps between sessions, the wider that gap gets. A 30-minute cold-start eval tells you almost nothing about how an agent with six months of ingested project decisions will handle a real migration. This is the same structural blindness as correlated judges scoring a session: you've measured one blind spot and called it a committee. Here you've measured one memory state (empty) and called it the model. The variable that actually drives production output, prior state, is exactly the variable the eval pins to zero. The fix doesn't have to destroy reproducibility. You can parameterize it instead: run a task three ways, empty context, a small hand-curated pr

The failure mode that matters in AI code review isn't a missed bug. It's output that reads like a real review but is structurally wrong, because that's the version you trust and act on. I keep coming back to a database-recovery writeup from Oskar Gross at Glazer. They used Codex to crack an obfuscated schema in a proprietary Cronos database and convert it to CSV. The surprising part was that getting the values out was not the hard part. Proving each value still sat under the correct column was. The line worth stealing for how you evaluate a review tool: a CSV containing readable values under the wrong headers would be worse than an obvious error, because it could look valid while being semantically corrupted. That is what an AI review gives you when it only checks whether the code reads well. It can flag a real surface issue and miss that the overall framing is off. Or it can bless a change that is coherent and wrong. The output reads fine, so you trust it, and the defect sits exactly where the tool told you nothing was wrong. Fluent and wrong beats obviously-wrong every time, because obviously-wrong makes you look. So when comparing review tools, weigh structural validation over apparent readability. Does the tool actually resolve the change against the codebase, or does it review the patch text in isolation? Does it check the change against surrounding types, contracts, and callers, or only that the lines scan okay? Can it tell you "this looks valid but violates the shape of the system," or just that the prose is fine? A tool that is fluent but structurally blind is more dangerous than a conservative one that says "not sure" often. The conservative one makes you look closer. The fluent one makes you stop. Benchmark the worst case, not the average. A mean bug-catch rate hides the region that decides whether you can trust the tool: the slice of changes where it produces plausible, authoritative, wrong feedback. Build your eval to surface exactly that, then decide.

When a user asks the LLM to perform an action, e.g., get the current weather or current stock market details, the LLM cannot get this information on its own. It needs some functionality or tools along with a description of when to call these tools/functionality. How is it working? The LLM analyzes the query to find out whether it is related to any of the tools and their descriptions. If so, the LLM will suggest the tool along with the parameters to the agent/external logic. The external logic will perform the action and send the result back to the LLM. The LLM reads the result and generates the structured output. Suppose the tool call is not matched; the LLM will derive the answer from its own knowledge. If we give an ambiguous description, the LLM may not generate the output properly. The description for the tool calls must be proper so that the LLM can semantically identify the tool and match the arguments. example description """ Add up two integer numbers. This function simply wraps the `+` operator, and does not do anything interesting, except for illustrating what is the docstring of a very simple function. Parameters ---------- num1 : int First number to add num2 : int Second number to add Returns ------- int The sum of `num1` and `num2` See Also -------- subtract : Subtract one integer from another Examples -------- >>> add(2, 2) 4 >>> add(25, 0) 25 >>> add(10, -10) 0 """

1. The Starting Line: The Last Non-Reasoning Flagships GPT-4.5, DeepSeek-V3, and Claude 3.5 Sonnet share something that has nothing to do with benchmark scores: they were the last major models built entirely on the "pretrain, then instruct-tune" recipe. All internal computation was done in one forward pass per token, with no backtracking, verification or revision mechanisms in place. By late 2024, these labs had run into the same big wall: scaling pretraining data and compute had reached a point of saturation. The naive recipe of more tokens, bigger model, more GPU-compute no longer bought equivalent capability gains, particularly on multi-step reasoning tasks. Good output quality became the bottleneck before parameter count did. What came after wasn't a bigger version of the same thing. It was a different training paradigm applied on top of the existing ones. The field pivoted to training models to think before they answer. 2. RL Terminologies Recap Think of a Roomba cleaning a house. The environment is the house; the state is what the Roomba currently senses (position, dirt map, obstacles); an action is a movement decision (turn, advance, suck); a trajectory is one full cleaning run; and the reward is some measure of how much dirt got picked up, dispensed along the way or tallied at the end. Map this onto an LLM generating text: the state is the prompt plus every token generated so far; an action is the next token (or, at a coarser grain, the next reasoning step); a trajectory is the full generated sequence; and the reward is a scalar signal applied to that sequence, either at the very end or, in some setups, at intermediate points. 3. The Pipeline, Oversimplified Pretraining → SFT → RLHF / RLVR Pretraining gives the model raw capability and world knowledge from next-token prediction over a huge corpus. SFT and the RL stages that follow are about shaping that capability toward useful, correct, well-formed behaviour. The rest of this post is a section-by-section zo

Artificial intelligence has solved a major mathematics problem, but credit for the accomplishment is murky.

OpenAI has published a formal account of an AI-assisted result on the Navier-Stokes Millennium Prize Problem, saying an internal system produced an analytical proof that three-dimensional incompressible Navier-Stokes dynamics can develop a finite-time singularity. The company also says GPT-6 Astra completed the Lean formalization used in its verification process. The announcement is notable not simply as a model benchmark, but as a reported example of AI being used across a demanding mathematical research workflow. In its September 8, 2026 formal Navier-Stokes Millennium Prize Problem write-up , OpenAI describes the result, links to a paper and Lean formalization, and explains the fluid-dynamics significance of finite-time singularity. The company frames the work as a separate research milestone from the wider GPT-6 Astra rollout. It also says it intends to recognize priority from Tristan Buckmaster of New York University and Levent Alpöge of Anthropic for concurrent related work. The distinction between the systems involved matters. OpenAI states that the internal model that generated the research result was significantly more capable than GPT-6 Astra . Astra's reported contribution was the additional task of expressing the proof in Lean, a formal proof language and environment designed to let computers check whether each logical step follows from defined rules. That makes the announcement a demonstration of a coordinated research process, rather than evidence that a publicly available GPT-6 Astra deployment independently solved the problem. What OpenAI reported about the research process According to OpenAI, training for its new internal model began around August 28, 2026. On September 1, rumors circulated that Millennium Prize Problems had been solved. The Navier-Stokes effort then used a coordinated system with on the order of 10,000 concurrent agents and ran for roughly 88 hours. OpenAI says the Lean formalization took about 17 additional hours using GPT-6 Astr

OpenAI has announced GPT-6 Astra , its next-generation flagship model, with a staged rollout across ChatGPT, the OpenAI API, Microsoft Azure , and AWS Bedrock. The September 3, 2026 release turns a previously signaled technical breakthrough into a defined product launch, centered on OpenAI's claims of stronger performance in computer use, browsing, software engineering, cybersecurity, science, and professional work. According to OpenAI's GPT-6 Astra launch announcement , Astra combines advances in pre-training, reinforcement learning, and alignment. OpenAI describes it as its most intelligent and aligned model, while accompanying deployment materials set out a phased access plan and additional controls for higher-risk cyber capabilities. For businesses, the most consequential detail is not only the model's stated capability range. It is the distribution plan. Astra is intended to reach both direct OpenAI users and teams that consume AI through established cloud platforms. That could make the model relevant to existing applications, development workflows, and AI-assisted operational processes, subject to access terms, pricing, and platform-specific controls that have not yet been fully detailed. What GPT-6 Astra changes OpenAI positions Astra as a generational step forward, rather than a minor model update. Its launch materials highlight performance across tasks that often require multiple forms of reasoning and action, including interacting with computers, using the web, writing software, and handling specialized professional work. The company also reports improved results over GPT-5.6 Sol on select benchmarks. The supplied materials do not provide a complete benchmark table or all underlying measurements, so the most accurate reading is that OpenAI is making a targeted performance claim, not that every task will improve by the same amount. Area GPT-5.6 Sol GPT-6 Astra Position in OpenAI's model line Previous model used as a comparison point in Astra materials Newly

A while ago now, I entered a Bad UX competition, where a prize went to the worst date-picker. Since then I have finally found some time to polish it and make it safe to release to the public; here it is . I didn't win the contest, so I guess my demo wasn't bad enough. Is that a compliment? The idea was to parody a future where AI assistants are so widespread and overused that they become a hindrance rather than a help to the user. In this case, the date-picker form element is intercepted by a bot who insists on entering the date for you, and must establish your date of birth through tedious questioning; it refuses to simply accept the date when told directly. Besides showing it off, I thought I'd share some of the challenges I faced and lessons learned. This was my first time building an AI-powered application and my first time using the OpenAI API. Original architecture Initially, the conversation worked like this: sequenceDiagram participant C as Client participant S as Server participant O as OpenAI C->>S: Start conversation Note over S: Create session in memory S->>O: System prompt O-->>S: "What season were you born in?" Note over S: Store conversation history S-->>C: Reply + session ID C->>S: "Around Thanksgiving" + session ID Note over S: Retrieve conversation history S->>O: System prompt + history + answer O-->>S: "Was it early or late November?" Note over S: Update conversation history S-->>C: Reply When the user focuses the date field, the client makes a request to initialise the conversation. This creates a chat session in application-server memory and sends an initial request to OpenAI (specifically gpt-4o-mini), along with the prompt defining the rules of the challenge. The assistant's first response, containing its greeting and opening question, is returned to the client along with the session ID. The client includes that ID with each subsequent answer, allowing the server to retrieve the corresponding conversation history. Each new OpenAI request inclu

OpenAI is bringing template-based marketing design into ChatGPT through its Adobe Express plugin. The integration gives users a guided way to create and customize common visual formats, including social posts, flyers, posters and invitations , using their own text, colors and images. For businesses that need campaign assets quickly, the practical change is a shorter path from an idea or prompt to an editable design. The capability is documented on OpenAI's Adobe Express plugin page , which says users can create marketing visuals in ChatGPT with professional templates and produce ready-to-use designs without design expertise. This is not simply image generation. The documented workflow combines a template starting point with editable design elements, making it better suited to assets that need clear messaging and recognizable branding. What the ChatGPT and Adobe Express integration provides Template-led design solves a frequent problem with generated visuals: a useful image alone may not be a finished marketing asset. A poster, flyer or social graphic usually needs readable copy, a deliberate layout and visual choices that fit the organization using it. Starting with a template gives those elements a structure that users can then adapt. According to OpenAI's plugin description, users can customize text, colors and images within professional templates. Adobe's wider Text-to-Template work also supports the broader idea that text prompts can help generate or adapt layouts. The specific OpenAI integration, however, should be understood through what its official page documents: creating marketing visuals in ChatGPT with Adobe Express templates and editing those core design components. Design format Included in the documented template flow Supported customization Social posts Yes Text, colors and images Flyers Yes Text, colors and images Posters Yes Text, colors and images Invitations Yes Text, colors and images A more practical workflow for marketing teams The value of a

Security gnomes are pumping out patches ahead of an expected onslaught of AI-assisted attacks.

Last month, a Claude user noticed his account was consuming tokens even though he wasn't working. Anthropic has since warned users about hackers.

Cognition's valuation multiple is higher than Cursor's was before selling to SpaceX.

OpenAI says it found a solution to a major math problem that has remained unsolved for around 90 years, as reported earlier by The New York Times and Wired. In a blog post on Tuesday, OpenAI announced that it discovered a solution to the Navier-Stokes problem - which relates to the flow of liquid and […]

The Exploration Company (TEC) has raised $450 million to build reusable spacecraft, in what it describes as “the largest-ever Series C by a European space company.”

"Science fiction imagines the future. We build it."