Eido Askayo

    Last month, I wrote that Project Glasswing mattered less as a model announcement and more as a deployment signal . Anthropic had a frontier model with unusually strong cyber capability, and instead of shipping it broadly, it wrapped it in a controlled defensive program. The new initial update for Project Glasswing makes that decision easier to understand. It does not prove vulnerability research is a solved problem. But it does show something important: AI-assisted vulnerability discovery is scaling faster than the human systems that verify, disclose, patch, and deploy fixes. What Changed In The First Month? Anthropic says its approximately 50 partners have already found more than 10,000 high- or critical-severity vulnerabilities across systemically important software. The public examples are notable: Cloudflare says Mythos Preview helped it find 2,000 bugs , including 400 high- or critical-severity issues, with a false positive rate its team consid...

Last month, I shared the open survey for the State of AI 2026, asking you to help replace the endless industry hype with actual, ground-level field data. The results are finally out, collected from 7,258 web developers . After reviewing the deep breakdown across every section—from models and agents to demographics and real-world pain points—the verdict is clear. AI-assisted coding is no longer a bleeding-edge experiment. It has crossed the threshold to become standard engineering practice. Here is what the data actually tells us about how software development is changing in 2026. ⚡ The 50% Threshold is Shattered In 2025, developers reported that AI generated roughly 28% of their code on average. This year, that figure has doubled to 56% . The heaviest adoption growth is occurring among developers who rely on AI for over 75% of their output. Interestingly, the data shows that higher reliance on AI correlates with fewer years of professional experience. But even for seasoned vet...

  I just added another feature to Kino , my educational, cinema-themed distributed systems project. The LangGraph-based agent service is one part of that platform. This time the feature was memory . But not the vague, hyped kind. I did not want Kino to pretend it knows a user's taste forever. I wanted something much narrower and much more useful: Short-term conversational memory for follow-up turns that helps the agent continue the current search without pretending to know more than it actually does That decision mattered a lot. It kept the feature small enough to trust, but still visible enough to feel like real agent behavior. What changed The new capability is simple to describe. A user can start with: Discover exactly 3 comedy movies from 2010 onward from Kino's catalog. Then follow up with: I didn't like them, please discover different ones. And Kino can continue from the current thread instead of tr...

  I just added a new feature to Kino , my educational movie-discovery project built with LangGraph: a prompt-driven discovery flow that finds grounded titles from a local catalog. The easy label would be RAG . More precisely, it is not classic RAG . What I built is closer to a structured-retrieval agent : the model interprets the request, a narrow service returns structured facts, and deterministic code enforces the final result. That distinction sounds academic at first. In practice, it changed almost every implementation choice. What I actually built Kino is an educational project, but this feature forced a very real architecture decision. I wanted a user to be able to type something like Discover comedy movies from 2010 onward from Kino's catalog. and get back grounded titles from the project's own data. The final flow is intentionally small: an LLM interprets the user's request a single search_titles tool turns that into a structured catalog query t...

 Last week, on April 23, 2026, Canonical released Ubuntu 26.04 LTS "Resolute Raccoon" . The interesting part is not GNOME 50 or the normal LTS cadence. It is that Ubuntu 26.04 moves more of the hard platform work into the supported distro path: TPM-backed full-disk encryption , CUDA via apt , ROCm in Ubuntu's repositories , Wayland with NVIDIA support , and Rust-based sudo and core utilities . For platform teams, the value is straightforward: less out-of-band setup, fewer per-machine exceptions, and a cleaner baseline across desktop, server, and WSL. Ubuntu 26.04 LTS matters because it turns several "extra integration projects" into supported platform defaults. What is Ubuntu 26.04 LTS? In plain English, this is the Ubuntu release for teams that want a long support window and fewer surprises. Canonical says Ubuntu 26.04 LTS will be supported until April 2031 , with up to ten years of ESM updates through Ubuntu Pro. If you are coming from Ubuntu 24.04 L...

On April 23, 2026, OpenAI introduced GPT-5.5 . The obvious story is performance: better coding, stronger tool use, more persistence, fewer tokens, and GPT-5.4-class latency. But the more important story is what came with it. OpenAI did not just ship a benchmark table. It shipped a system card , a long deployment-safety write-up on misalignment and internal deployment , and a public Bio Bug Bounty . That bundle matters. GPT-5.5 is one of the clearest signs yet that frontier AI launches are becoming deployment packages: model + tools + safeguards + access policy + post-launch testing. What is GPT-5.5? In plain English, GPT-5.5 is OpenAI's current answer to "real work" AI. Not just chat. Not just autocomplete. A model that is supposed to take messy tasks, plan them, use tools, check its own work, and keep going across terminals, browsers, docs, and spreadsheets. The launch post makes that pitch directly, and the benchmark table supports part of it: 82.7%...