标签：#AI

Every agent-security vendor tells you what they block. Nobody tells you what they miss. That gap is the whole problem. "We stop prompt injection" is a claim you cannot check. You cannot run it, and you cannot tell it apart from the next company saying the same sentence. So security engineers do the rational thing and discard all of it. I published the opposite. It is called the ARE Incident Database , and it is public: https://aredb.org What is in it 32 agent failures that actually happened, each with a real source. A production database dropped during a code freeze. Twenty-five thousand documents deleted in the wrong environment. Credentials read and shipped to an external sink. A budget burned to zero in a loop. Each one gets a stable id ( ARE-2026-001 through ARE-2026-032 ), and each one is mapped to its category in the OWASP Agentic Security Initiative Top 10 , which is the peer-reviewed catalog of what goes wrong with agents. AREDB does not compete with it. OWASP owns the map. This is the cited incidents underneath it. The part that makes it uncomfortable to publish Every entry carries a coverage flag, and the flag is about our own product . We block 23 of the 32 today. Two more are partial, and they say partial. That leaves six of the ten OWASP categories covered at the action layer, and four that we do not cover: ASI06 Memory and context poisoning. We strip the hidden characters attackers use to smuggle instructions into text. We do not read the meaning of the text itself, so this one is only partial, and we mark it partial. ASI07 Insecure inter-agent communication. This is about how agents talk to each other over the network, which a firewall that sits in front of actions never sees. Not ours. ASI09 Human-agent trust. This is a design and disclosure problem. There is no action for a firewall to catch. Not ours. ASI10 Rogue agents. We stop the dangerous actions, but we do not diagnose the misbehavior itself. Partial. A firewall that claimed all ten would be l

If you are like me and use AI often to help with complicated tasks and own a Flipper Zero, you might want a Super Tiny Language Model (STLM). Why this is useful Until now, there have been no language models small enough to run on a small device, such as a Flipper Zero. Flippermind relies on lightweight Python3 installs and a local connection to Qwen2.5-0.5B. Although this is configured for Flipper Zero, you can run it on other small machines like a Raspberry Pi. Challenges Throughout development, I found errors within the language model not loading. PyTorch and Python3 transformers are used to run the Python3 dependencies, and they would not run on Debian Linux distros. I fixed the install.sh to run on any OS. How it works There are a couple main components that make this function. The main function is the qwen_2_5_ask.py script located in tools/ . This is script runs the language model when asked a question. Example: python3 qwen_2_5_ask.py "Hello world" The install.sh file located in main works as the installer for the model. You run it using: bash install.sh You can download it on the GitHub repo linked below. How to contribute To help me continue to work on this, check out our Github Repo If you have questions or just want to talk, talk to me in the comments or email me at hello@syop200.com What do you guys think? Any ideas? I'm curious if anyone has successfully ran other quantized models on hardware with this little RAM—let me know in the comments!

When the enemy occupies favorable terrain, don't attack head-on. Use a decoy to lure the tiger down the mountain. Then take the mountain. — The 36 Stratagems, Lure the Tiger Down the Mountain Previously on this series: #2: Derek Shaw Walked Into Another AI Promise. The Pipeline Had a Better Plan. — Derek lost the Finova contract at QualiGuard. In the parking lot, Lena turned back before getting in her car: "Next time you put together a proposal — make sure your boss knows what you're doing out there." That line followed him. At MediSys he nearly made the same mistake. Fixed the ETL pipeline instead of the model, beat OmniDx with their own white paper. But VP Morgan still saw through him: he still hadn't told his boss. #8: Alex Watched an AI Dashboard Take Over. He Kept the Keys Under the Table. — MedTech signed a seven-figure AI operations monitoring system. Alex was assigned as training lead. Under everyone's noses, he built a second monitoring panel labeled "training environment." Three weeks later the vendor dashboard went down. Alex's hidden panel was the only one still running. The first time Alex and Derek really talked, and it wasn't in a working group. At the FHIR standards committee quarterly meeting, they sat in the same row, three seats apart, and voted against the same proposal. They knew each other's names. That was it. Two months later, Derek was fixing a partition config in the staging environment. MediSys had just signed a major hospital; the AI diagnostic validation platform was in integration testing. He found an unrotated log directory in /etc/logrotate.d/ with a prefix that didn't match MediSys's naming convention. He traced it upstream. One server. Labeled "temporary data exchange node." MedTech's supply chain order stream and MediSys's diagnostic validation records were sitting in the same directory. Write permissions hadn't been restricted. The hospital's integration spec had a line saying "both parties are recommended to complete data alignme

By keeping the number of investments to about 25 per fund, Greylock aims to remain what it calls "the most important partner" to its founders.

I wanted to play with AI image generation without paying for Midjourney or dealing with Discord bots. So I built SnapShift — a free web tool that transforms photos into artwork in seconds. What it does: Upload any photo (portrait, pet, landscape, product) Pick from 50+ styles — cyberpunk, anime, oil painting, Ghibli, movie posters, 3D figurines, and more Download in 1K or 2K quality No signup, no limits, completely free Tech stack: static site on GitHub Pages, AI generation via Agnes API with Cloudflare Worker proxy. Try it: https://snapshit.fun Would love to hear what other styles you'd like to see!

Every "build an AI app" tutorial stops at the demo. Prompt goes in, response comes out, ship it. Nobody covers the part where that demo has real users and you're staring at a Gemini or OpenAI invoice trying to figure out which feature did that. I've spent the last several months building the AI layer for a consumer app that fires vision and language calls on almost every user action. Not a chatbot getting occasional traffic. A product where the model call basically is the product. Here's what I actually had to build, in the order I had to build it. Four problems, not one Cost first, obviously. Tokens are metered, and past a certain volume, calling the model on every request means paying for answers you already gave someone five minutes ago. Latency next. A cache hit lands in milliseconds. A cold model call takes seconds. Users feel that, especially anything camera-driven where they're staring at a loading spinner over their own kitchen counter. Reliability too. Your provider will have an outage or a degraded day at some point. Not if, when. And blast radius. One bug, one bot, one traffic spike, and a $50/day bill becomes a $5,000/day one while everyone's asleep. You don't see any of this in a demo. It shows up with real traffic, and by then it's a lot more expensive to fix than it would've been to build right the first time. Cache on what the query means, not its exact string Key your cache off the literal request text and you've built something close to useless. "What can I make with chicken and rice" and "chicken and rice, what should I cook" mean the same thing and share almost no characters. So embed the query, run a vector similarity search against everything already answered, and if something clears a high threshold, serve that instead of paying for another call. I use 0.95 cosine similarity as the bar. async function checkSemanticCache(embedding: number[], taskType: string, threshold = 0.95) { const { data } = await db.rpc("find_similar_response", { query_emb

Não é novidade para ninguém que estamos passando por uma transformação na área de desenvolvimento de software, em que a IA está assumindo diversas atividades. E isso me faz pensar: o que vamos medir, ou o que teremos como parâmetro para qualidade de software daqui pra frente? É sobre isso que vou falar neste texto. Antes das métricas: entenda o momento do seu time Antes de entrarmos nas métricas em si, precisamos entender o momento em que o nosso time está. É muito fácil eu simplesmente jogar métricas aqui e você aplicá-las ao seu time de maneira automática — mas será que elas fazem sentido para o seu contexto? Uma coisa que eu falo bastante aos meus alunos da mentoria que dou na He4rt Developers é: pra que eu quero isso? Softwares representam necessidades do mundo real, logo, medir o sucesso e a qualidade deles vai depender muito das necessidades que eles buscam suprir. Partindo agora para as métricas, eu gosto de dividi-las em dois grupos: Métricas para stakeholders Métricas para o time Qualidade de software não se resume a número de bugs — ela se aplica tanto em como o software é recebido pelo cliente final, quanto em como ele é desenvolvido. Métricas para stakeholders Uma coisa que eu aprendi neste tempo na empresa em que tenho atuado, principalmente com a transformação digital, é que mostrar número de bugs abertos ou resolvidos não mostra para o público o que realmente importa: como está a qualidade do produto. E, para me ajudar nisso, eu sempre tento me colocar no lugar de um cliente que não tem conhecimento profundo sobre o ciclo de desenvolvimento de software. A primeira coisa que eu gostaria de ver quando um QA, ou o time, vier me mostrar os resultados de uma sprint ou de um quarter é: quantos problemas eu tenho em produção — mas não só isso, quanto tempo tenho levado para resolvê-los. Mean Time to Resolve/Repair (MTTR) Essa é a famosa métrica que vai mostrar o tempo que leva desde que o problema é identificado até ele ser resolvido em produção. Dependendo

​I. The Terminal Velocity of Disembodied Intelligence ​The current paradigm of artificial intelligence is approaching a fundamental cognitive ceiling. We have built vast, sprawling minds, yet we have trapped them in sensory deprivation chambers. Today’s Large Language Models and industrial robotics operate under a fatal flaw: the reliance on linear, arbitrary tokenization. Traditional AI chops raw text into disconnected, non-semantic fragments, wasting immense computational memory tracking the mere phonetic order of spelling and grammar. ​Simultaneously, industrial machines navigate the physical world through siloed sensor pipelines. They process light, space, and kinetics as heavy, raw digital arrays, relying on brute-force algorithmic equations to stitch reality together. This is not intelligence; it is computational exhaustion. To achieve true artificial general intelligence, the machine must stop reading about the world and begin to inhabit it. ​II. The Biological Imperative ​Nature solved the hardware-software integration problem millions of years ago. Human biology remains the ultimate benchmark for flawless, metabolic efficiency. Our somatosensory system does not wait for a central brain to read a text string before pulling a hand away from a fire; it processes physical feedback in milliseconds through decentralized neural highways. ​Human cognition leverages cross-modal integration within the angular gyrus, allowing visual data to be instantly cross-referenced with kinesthetic feedback to trigger anticipatory motor reflexes. Furthermore, biological intelligence is inherently tied to physical survival. We manage thermodynamics through a centralized hypothalamic thermostat, protecting the body from cellular destruction, while routing ambient light data to deep-brain structures to drive a natural circadian rhythm. True AGI requires this exact synthesis of abstract thought and biological mechanics. ​III. The Logographic Paradigm Shift ​To bridge the gap between

If you've been working with cognitive architectures that rely on structured memory injection, you likely know the pain of corrupted or incomplete embedding spaces. The latest update to hermes-memory-installer directly addresses a brittle failure mode: missing embeddings in the gbrain module. This fix introduces an automatic, targeted repair mechanism that detects and rebuilds only the affected subset of embeddings, rather than triggering a full reinstall. Here’s what changed, why it matters, and how to benefit from it. The Problem: Silent Degradation in gbrain In a typical setup, hermes-memory-installer populates the gbrain—a specialized long-term memory store—with precomputed embeddings for core concepts, episodic traces, and procedural patterns. These embeddings are the numeric backbone that allows the agent to query, retrieve, and associate memories efficiently. However, under certain conditions—partial upgrades, concurrent memory imports, or incomplete network transfers—the gbrain’s embedding table ended up with holes. Specific embeddings for targeted contexts were simply missing. The agent would still boot, but retrieval quality degraded silently: queries returned null vectors or fell back to generic responses, breaking fine-grained recall. Users reported that their agents "forgot" recent conversations or failed to recognize learned skills, yet no obvious error was raised. Previously, the only remedy was a full reinstall of the memory installer, which wiped and rebuilt the entire gbrain. That was slow, wasteful, and could erase customized embeddings that were working correctly. The Fix: Targeted Auto-Repair The new update ( v2.1.0 onwards) adds a dedicated repair pass during the installation and upgrade routine. Instead of scanning the entire gbrain, the installer now maintains a lightweight manifest of expected embedding keys for each memory context. During setup, it checks the actual embedding store against this manifest. If any keys are missing, it triggers

Microsoft is looking to sell its in-house AI models as more efficient and cost-effective than its competitors' models.

Across 101 enterprises, agent orchestration is consolidating onto model-provider platforms — Anthropic’s Claude leads by a wide margin — chosen for the gravity of the underlying model and judged on reliable multi-step execution. But the ambition runs well ahead of the reality: most deployed “agents” are still chatbot wrappers, the control plane enterprises expect is deliberately hybrid to avoid lock-in, and real-time fiscal control over token burn remains the exception. This wave of VentureBeat Pulse Research examines enterprise agent orchestration: which platforms enterprises run on, what drives the choice, what they optimize for, how they expect agent control to be structured, and — most revealingly — how orchestrated their deployed “agents” actually are and how tightly they control the cost of running them. The central finding is a gap between orchestration ambition and orchestration reality. Enterprises are consolidating fast onto the major model platforms: Anthropic’s Claude is the primary platform for 40%, more than double any rival, followed by Microsoft (18%) and OpenAI (13%). The choice is driven by “model gravity” — native alignment with a state-of-the-art base model (21%) — and success is judged by reliable, multi-step execution (task completion reliability 32%, multi-step workflow management 28%). Yet asked to assess their portfolios honestly, 71% say a quarter or fewer of their deployed “agents” are true multi-step orchestrated workflows rather than single-prompt chatbot wrappers, and only 10% have crossed the halfway mark. The orchestration layer is being built well ahead of the orchestrated portfolio it is meant to run. That gap shapes the architecture enterprises are putting in place. By the end of 2026 a clear majority (51%) expect a hybrid control plane — provider-native plus external orchestration — and only 6% expect to hand control to a provider-managed service, because vendor lock-in (35%) is the risk they fear most if control lives inside a mo

Valve has been incredibly friendly to customers who need repairs - which is why it was so surprising to hear that Valve was already discontinuing the battery for the Steam Deck LCD handheld. It would have meant you could no longer just buy a battery from iFixit and install it yourself. Why would Valve stop […]

There is no reliable “magic button” that turns an arbitrary HTML website into a clean, responsive, fully editable Elementor project. At first glance, converting an HTML website to WordPress sounds like a file-format conversion. You already have the design, text, images, CSS, and JavaScript. Why not upload everything, click Import, and continue editing the page in Elementor? The problem is that HTML and Elementor do not describe a website in the same way. An HTML page is the final output: a tree of elements styled by CSS and controlled by JavaScript. Elementor stores an editable model made of containers, widgets, global styles, responsive settings, and WordPress-specific data. A browser can render both results so that they look similar, but their internal structures can be completely different. What automated converters can do Modern converters and AI tools can read HTML, identify visual sections, and generate a rough WordPress layout. They are useful for prototypes and simple landing pages. Some tools can also copy styles or place the original code inside an HTML widget. But visual similarity is not the same as a production-ready Elementor website. A converted page may look acceptable on one screen while still containing: deeply nested containers; duplicated CSS; fixed pixel dimensions; broken mobile layouts; inaccessible elements; content that a client cannot edit. Forms, menus, sliders, animations, dynamic content, and custom JavaScript usually require separate work. The real challenge is rebuilding meaning, not copying pixels A human developer does not only see a rectangle with text. They need to decide whether it should become a Heading widget, a reusable global component, a dynamic WordPress field, or part of a template. The same applies to the rest of the page: Navigation must work with WordPress menus. Forms need validation, delivery actions, and spam protection. Repeated content may need posts, custom fields, or WooCommerce products. Fonts, colors, spacing,

It's likely to become a direct power source for SpaceXAI's data centers.

The Elon Musk-owned xAI is suing a South Carolina man who allegedly used the company's Grok AI chatbot to generate child sexual abuse material (CSAM). In a lawsuit reported earlier by Reuters, xAI claims Terry Wayne Harwood "knowingly and intentionally used Grok to circumvent safeguards, alter nonconsensual images, and generate and distribute CSAM," breaching the […]

For five years, the answer to "how do we make the model better" was always the same: bigger model, more data, wait for the loss curve to bend. Scaling laws made AI progress feel almost boring — predictable, like compound interest. That knob still works. But it's not the interesting one anymore. The real question teams are fighting with now is: given a model you already have, how much compute should it spend per question? Not per training run. Per question. And the answer turns out to break almost every assumption your inference stack was built on. The single forward pass was always the weak link Here's the thing nobody says out loud enough: a model's first answer is often just a noisy sample. The right answer was in there somewhere — it just wasn't the loudest one. You don't fix that with more pretraining tokens. You fix it by sampling more, checking the work, or letting the model take a second pass. That's the whole idea behind test-time compute. And there are three ways to spend that extra compute, and they are not interchangeable: Let it ramble longer. Cheap. Also the reason some models talk themselves out of the right answer after 3,000 tokens of unnecessary detours. Generate N answers, vote. Parallel, simple, plugs into infra you already have. Also: returns drop off fast. Past a certain N you're burning GPU-seconds for crumbs. Search a tree of partial reasoning steps, prune the bad branches early with a verifier. The most compute-efficient option by far. Also the one that quietly breaks your serving stack. Most real systems end up doing all three — cheap by default, escalating only when a confidence check says "this one's hard." That escalation decision is its own piece of architecture. Most teams bolt it on as an afterthought. It shouldn't be one. Your serving stack was not built for this Continuous batching, paged KV-cache, speculative decoding — all of that infra assumes requests are independent, similar in length, and finish in one pass. Test-time compute v

A 90% discount doesn't come from nowhere. Someone pays for it, and usually that someone is you: with your data, with model quality, or with somebody's stolen account. An entire industry has grown up around Anthropic and OpenAI over there. They call them "transfer stations": proxies that resell tokens from Western models. This stopped being a garage workaround for rate limits a long time ago. It's a market with its own economics, and it runs on a few things. Accounts. These services mass-produce free and Max subscriptions through stand-in registrants and other people's KYC, then wrap them in a pseudo-API. Model swapping. You pay for the flagship, but your simple request gets quietly routed to something cheaper. Who actually generated the answer? You can't check. Data. The proxy operator sees all of it: prompts, responses, code, internal context. Even if they never sell the logs, you don't know where those logs sit, who reads them, or whether they ended up in somebody else's training run. Take Yunwu. By their own price list, access to Anthropic models runs up to 93% below official. The price alone proves nothing. But the market logic is simple: restrictions don't kill demand, they make it more expensive, and they feed the workaround industry. In June 2026 Anthropic accused entities tied to Alibaba/Qwen of a campaign to siphon Claude: nearly 25,000 fake accounts and 28.8 million requests in six weeks, to distill their own models. DeepSeek, Moonshot and MiniMax have caught similar accusations before. Same grey access, just wholesale. But the problem is bigger than China. Even with fully legitimate access, most teams can't answer three questions about their own production: which model actually served the request, what it cost, and whether quality dropped. The grey market just takes that blindness to its logical end. I think the priorities here are upside down. For a serious product, production visibility and control over your data matter more than a few percent saved on

The AI Revolution: How Artificial Intelligence is Redefining the 2026 FIFA World Cup The FIFA World Cup has always been a spectacle of human skill, passion, and drama. However, the upcoming 2026 tournament is set to be something entirely different: the world’s first "AI-native" major sporting event. Through a strategic partnership between FIFA and Lenovo, artificial intelligence is being woven into the very fabric of the tournament. This isn't just about fancy graphics on a screen; it is a fundamental shift in how the game is officiated, how teams prepare, how the tournament is managed, and how billions of fans experience the magic of football. By focusing on "democratization" and "operational intelligence," AI aims to level the playing field for all 48 participating nations while managing the massive logistical challenge of hosting matches across three countries and 16 different venues. Precision on the Pitch: Revolutionizing Officiating One of the most high-pressure aspects of football is decision-making. In a tournament of this scale, a single millimeter can be the difference between a goal and a miss. AI is stepping in to ensure transparency and accuracy through two major innovations: 3D Player Avatars and Advanced SAOT To make Semi-Automated Offside Technology (SAOT) more accurate, every one of the 1,248 participating players underwent a rapid 3D body scan. In just one second, technology created highly accurate "digital twins" of each athlete. Unlike previous generic models, these lifelike avatars replicate individual body shapes and dimensions. This allows for millimeter-accurate tracking, providing officials and fans with realistic 3D animations during offside replays that are much easier to understand. The "Referee View" Fans often want to see what the officials see, but traditional body cameras can be too shaky to watch. Using Lenovo’s custom AI-powered synchronization and stabilization engine, the footage from headset-mounted cameras is processed in real-t

I pin model IDs on purpose. Floating aliases have burned me before — a silent swap under a -latest tag once changed a tool-calling detail in production and cost me a Saturday with git bisect and a coffee I didn't enjoy. So everything I run points at a dated snapshot. gpt-5-codex was one of them. Last week I finally read OpenAI's deprecations page top to bottom instead of skimming it, and there it was: gpt-5-codex , retiring 2026-07-23 , sitting quietly next to ten of its neighbors. As I write this it's July 16. That's seven days . If you pinned any of the snapshots below, consider this your heads-up. Here's the full retirement list, how to find out in about ten minutes whether you're exposed, the replacement map with the things I'd actually regression-test, and the one structural change that turned my last forced migration from scary into boring. The shutdown list (retiring 2026-07-23) Straight from OpenAI's API deprecations page . I checked each row against the source while writing this — do the same before you act on it, because dates and replacements do get revised. Retiring model OpenAI's suggested replacement gpt-5-codex gpt-5.5 gpt-5.1-codex gpt-5.5 gpt-5.1-codex-max gpt-5.5 gpt-5.1-codex-mini gpt-5.4-mini gpt-5.2-codex gpt-5.5 gpt-5-chat-latest gpt-5.5 gpt-5.1-chat-latest gpt-5.5 gpt-4o-search-preview-2025-03-11 gpt-5.4-mini gpt-4o-mini-search-preview-2025-03-11 gpt-5.4-mini gpt-4o-mini-tts-2025-03-20 gpt-4o-mini-tts-2025-12-15 computer-use-preview-2025-03-11 computer-use-preview (or gpt-5.4-mini ) Note the shape of it: the *-codex line collapses into gpt-5.5 — except gpt-5.1-codex-mini , which drops to gpt-5.4-mini — the chat-latest aliases fold into gpt-5.5 too, the two *-search-preview snapshots move to gpt-5.4-mini , and the two preview families ( tts , computer-use ) just roll to a newer dated snapshot / the undated alias. Once you see the buckets, the migration is less intimidating than the eleven-row table looks. Find out if you're exposed (about ten m

Every GEO ("generative engine optimization") tool, including ours until recently, sells some version of the same pitch: fix your robots.txt, add Schema.org markup, write FAQ schema, and AI engines will cite you more. We build one of these tools — Causabi scans sites for AI-crawler readiness and generates fix files (robots.txt, llms.txt, JSON-LD, FAQ blocks). As part of validating our own scoring weights, we ran the numbers on whether the score actually predicts getting cited. Short version: it mostly doesn't, once brand prominence is in the picture. What we measured We scored 44 domains on a 6-category on-site readiness algorithm: robots.txt (AI bots allowed or blocked) Schema.org (Organization/LocalBusiness JSON-LD completeness) FAQ schema (FAQPage markup, 3+ entries) content depth/structure brand/NAP signals freshness (dateModified, recency) Then we checked how often each domain actually got cited by an AI engine (Claude, via its web-search tool, one measurement window, a fixed prompt set per domain). What we found On-site score vs. citation rate: Pearson r ≈ -0.08, Spearman ρ ≈ -0.03. Functionally no correlation — if anything, a very slight negative one, which is more likely noise than a real inverse relationship at this sample size. 86% of the 44 domains got zero citations in the window, independent of their score. The domains that did get cited clustered almost entirely by brand prominence — well-known domains got cited at a noticeably higher rate (~0.16 of prompts) than everyone else (~0 for the rest of the sample), regardless of how well-optimized their markup was. Why I'm not overselling this n=44 is small. This is an internal validation exercise for our own product, not a peer-reviewed study, and I don't want it read as one. Specific caveats: Single engine (Claude) this round. Citation behavior differs meaningfully across ChatGPT, Gemini, Grok, and Perplexity — we haven't run the same check across all four yet. One time window, no longitudinal before/after.