Production incidents almost never break in one place. The alert fires in one tool. The broken deploy is in Netlify. The suspicious change is in GitHub. The stack trace is in Sentry. The human context is in Slack. The runbook is in Notion. The "is this actually paging someone?" answer is in PagerDuty. A normal chatbot can sound helpful in that situation. It can say things like "you should check your recent deployments" and "look for related errors in Sentry." But that is not triage. That is a polished to-do list. I wanted something more useful: an agent that could go get the evidence, connect the dots across sources, show its work, and give an operator-grade answer grounded in real system data. The design constraint from the start was simple: no evidence, no answer. That became ReefWatch , a Coral-powered production triage agent built to investigate instead of improvise. It discovers the tools connected to a workspace at runtime, queries them as evidence, correlates records across systems, and produces a compact answer only when the facts support one. Coral became the backbone because it turns the messiest part of agent tooling into something the model can actually reason about: SQL . What This Guide Builds By the end of this route, you will have a blueprint for an agent that can: discover connected Coral sources at runtime query production systems through read-only SQL correlate evidence across code, deploys, errors, alerts, chats, and runbooks stream every query and row count into an inspectable UI run the same investigation workflow from a CLI when you want a scriptable path generate an incident report only when the evidence supports one stay focused with policy layers instead of a giant prompt blob In one sentence: ReefWatch is a Coral-powered investigation workspace that lets an agent discover connected tools at runtime, query them with read-only SQL, stream the evidence trail, and generate an incident report only when the facts actually support one. Why Coral B

Hi everyone!! I really wanted to share my research what I've been working on. I wanted to build a nn that can simulate games, or at least start doing that Most video generators are too large to run on consumer hardware realtime, so I I designed a model that does this from scratch. No fine tuning bs or anything The core de noiser network is fully trained from scratch to support this goal. From image to games data. That video. above is on a RTX 5090. The nn is a small Transformer-like model and works in a causal way, just like LLMs. That lets us KV Cache all past information and do a simple autoregressive decode forward passes for every new frame we want. In the video shared, the model is a 0.4B variant with some SIGNIFICANT ISSUES like poor motion and some weird flashes, some context issues It's taking the keyboard actions I give it in realtime and utilising that in the forward pass. (no classifier free guidance though) Im training the next iteration , a 0.8B model now. Btw I haven't done quantisation yet, that can save a LOT more time. bf16 is slow. submitted by /u/lucidml_lover [link] [留言]

submitted by /u/Kriem [link] [留言]

On May 29, 2026, OpenAI published its Frontier Governance Framework — and most developers moved on to the next item in their feed. That’s a mistake worth correcting. The document doesn’t announce a new model or lower an API price. It describes how OpenAI measures whether its own systems could enable mass-casualty events, what access controls gate who can reach those capabilities, and how this maps to the regulations — the EU AI Act and California’s Transparency in Frontier AI Act — that are actively shaping compliance requirements for any enterprise deploying frontier AI this year. If you build security tools on OpenAI APIs, the framework’s Trusted Access for Cyber program directly affects what your application can and cannot do. If you operate in a regulated environment, the framework is the vendor-side accountability document your compliance team needs to reference. And if you build on frontier models at all, the risk tier system in this framework governs the capability restrictions you will encounter — and, increasingly, what auditors and procurement teams will ask about when vetting your AI vendor stack. What the Framework Actually Is The Frontier Governance Framework is OpenAI’s published methodology for evaluating the risk profile of frontier models before and after deployment. It covers six functional areas: risk assessment and mitigation, model reporting, security risk management, incident response, external expert input, and framework updates. Each area has defined processes, thresholds, and accountability mechanisms. The core architecture is a tier system applied across four risk domains. Each domain is evaluated independently, with tiers reflecting capability levels that could enable specific categories of harm. A model’s rating in any domain determines what deployment controls apply — what gets blocked at the API layer, who gets elevated access, and what triggers an incident response workflow. The framework was published explicitly to align with two regu

Hiring an AI Development Company? Ask These 7 Questions First Most AI projects fail long before deployment. Not because the model is bad. Because teams skip the hard engineering questions. If you're evaluating an AI development company, ask these 7 questions first: 1. How is data secured? AI systems process sensitive business information. Ask: Where is data stored? Is encryption enabled at rest and in transit? Who has access to prompts, logs, and embeddings? Are enterprise security standards followed? Security should be designed in from day one. 2. What observability exists? You can't improve what you can't monitor. A production AI system should include: Request tracing Prompt/version tracking Latency monitoring Cost visibility Error reporting If nobody can explain what happened after a bad output — that's a problem. 3. How do you handle model drift? AI performance changes over time. Questions to ask: How are outputs evaluated? Is feedback collected? How are prompts/versioning managed? What happens when accuracy drops? Production systems need iteration loops. 4. What happens during failure? No system is perfect. Ask: Is there fallback logic? Human review? Retry handling? Graceful degradation? Failure handling matters more than demos. 5. How is access controlled? Enterprise AI systems require permissions. Examples: Role-based access API authentication Audit logs Team-level controls Not everyone should access everything. 6. What compliance assumptions exist? Especially important for regulated industries. Ask whether the system considers: GDPR SOC2 HIPAA Financial or internal compliance rules Compliance cannot be an afterthought. 7. Who owns the infrastructure? Clarify ownership before signing anything. Ask: Who owns the source code? Cloud infrastructure? Models and prompts? Data pipelines? You should avoid vendor lock-in. AI success is rarely about flashy demos. It's about secure infrastructure, reliability, observability, and long-term maintainability. What question

been maintaining a privacy first clipboard manager chrome extension for a while. recently shipped a snippets feature where you type /keyword in any text field and it expands to whatever you saved i use it for: - my product pitch so i dont rewrite it every time - boilerplate comments on github or pr reviews - pasting my product link into discussions - common replies i send over and over it supports two types - rich text with formatting for things like emails, and code with syntax highlighting if you need it. the expansion works through a content script on all websites. comes with a management page where you can search filter by type or language and edit your snippets. everything is saved locally only on your device, currently these are not synced, let's see if user needs the sync facility for these or not. NOTE: currently figuring out how to make this feature work on Google Docs and MS Word submitted by /u/nhrtrix [link] [留言]

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built I revisited my personal website and decided to turn it into a more complete and polished portfolio. The project originally started as a simple personal website hosted on GitHub Pages. While it was functional, many planned features and improvements were never completed. For this challenge, I am working on improving the mobile experience, adding dark mode support, enhancing the Projects section, improving SEO, fixing existing issues, and making the website more interactive and professional. My goal is to transform an unfinished personal website into a modern portfolio that better represents my work, skills, and projects. Demo Live Website https://ehsankahrizi.github.io/ GitHub Repository https://github.com/Ehsankahrizi/Ehsankahrizi.github.io Current Status This project is currently being improved as part of the GitHub Finish-Up-A-Thon Challenge. Planned improvements include: Better mobile responsiveness Dark mode support Enhanced Projects section Improved SEO More interactive user experience Better website performance Additional pages and content Fixing existing usability issues The Comeback Story When I returned to this project, I realized that many ideas I originally had for the website were still unfinished. Although the website was online, it still had several limitations: The mobile experience needed improvement. Dark mode was not available. The Projects section was incomplete. The contact functionality needed attention. SEO optimization was missing. The website relied on a mostly single-page structure. User interactions were limited. Performance could be improved. Instead of starting a new project, I decided to revisit this existing one and finally complete the improvements that had been postponed. This challenge provided the perfect opportunity to continue development, clean up the codebase, improve the user experience, and turn the website into something I can confidently share with ot

I lost more than 60 days but I made a huge update All OnemanBSD OS is now 64 bits. I also removed all bloat like Rust, Wayland and Qt dependencies from all app ports used in the system. (mpv was changed to gnome-mplayer because of that). New apps included (+ fixed source code of everything): audacity (audio editor) godot (game editor) deadbeef (mp3 player) ted (word processor) Also some bug fixes. Here is the link again: http://bialamusic.com/onemanBSD/ Here are my thoughts: I feel that we as humans are loosing control over technology that was created by humans. This is rising some serious and strange questions. I think we now need tools to power the individual so we can fight back. Not corporations. Not governments. Not organizations. Consider this my bullet in this battle. It is now in your hands so you can be active developers not just passive users.

The Journey I set out to build an efficient automation backend utility designed to classify incoming customer inquiries and automatically suggest optimized replies. My goal for this challenge was to document the transition of a basic, fragile text-parsing utility into a highly resilient, enterprise-ready application script. Production Evolution The Before (Fragile Prototype Base): The initial design relied entirely on rigid, case-sensitive string matching. It contained no persistent database engine, meaning analytics data was lost instantly upon script termination, and it lacked structural exception guarding, making it highly susceptible to standard runtime crashes if empty or unexpected values were inputted. The After (Fault-Tolerant Enterprise Core): I fully refactored the utility's entire architectural layout. The system now initializes a local SQLite relational table structure ( analytics.db ), appends automated date/time timestamps for granular metrics tracking, leverages strict array-based substring evaluation, and traps fatal exceptions seamlessly via localized error handling catch loops. Public Code Repository The complete operational source code, database persistence schemas, and functional verification blocks can be inspected directly via my public repository: https://github.com/playsmai/biz-responder-automation-

Anthropic just announced Claude Opus 4.8 , a major upgrade to their flagship AI model. If you use AI tools to help you write, debug, or architect software, this release has some huge updates that will change your daily workflow. The best part? It is available right now for the exact same price as Opus 4.7. Here is a quick, no-nonsense breakdown of what is new and why you should care. 1. Smarter Coding and "4x Better Honesty" We have all been there: an LLM confidently hands you a block of code, claiming it’s perfect, only for you to find out it breaks completely. Anthropic spent a lot of time fixing this "false confidence" problem. According to their internal testing, Opus 4.8 is four times less likely to let bugs or flaws in its written code pass by unremarked. It has better judgment, meaning it will actually question a bad plan, catch its own mistakes before showing them to you, and admit when it is uncertain about an edge case. 2. Parallel Coding with "Dynamic Workflows" Available in research preview for Claude Code (Enterprise, Team, and Max plans), Dynamic Workflows allows Claude to break a massive programming task down into smaller pieces. Instead of tackling a codebase line-by-line, it can spin up and run hundreds of parallel subagents at the same time to solve large problems. Anthropic notes that it can manage codebase-scale migrations across hundreds of thousands of lines of code from start to merge, verifying everything against your existing test suites. 3. New "Effort Control" Slider You can now manually choose how much processing power Claude puts into a task on Claude.ai and Cowork: High Effort: Claude thinks longer, reasons deeper, and double-checks its work. Best for complex architecture, tricky debugging, or heavy logic. Low Effort: Claude replies much faster and conserves your token rate limits. Best for quick syntax checks, simple explanations, or boilerplate code. 4. Developer API Upgrades If you are building products on top of Claude's API, Amazon

The tech industry is currently in a frenzy. Everyone is rushing to build the next big AI application, slapping a chatbot interface onto a database and calling it a day. But in this gold rush, we are leaving something critical behind: Enterprise Security. Living in a Kali Linux environment and spending time hunting vulnerabilities teaches you one fundamental truth: security is entirely about context. Hardcoded API keys, undocumented access escalations, and compliance blind spots remain the number one cause of major data breaches. Security teams don’t just need a chatbot that can answer questions; they need a single pane of glass. They need a Security Operations Center (SOC). For the Pirates of the Coral-Bean Hackathon (hosted by Coral and WeMakeDevs), I decided to tackle this massive industry problem. Over the course of 4 sleepless nights, I built CoralSec Copilot—an AI-powered, unified Enterprise SOC platform. Here is the complete Captain's Log of my entire journey, the architecture, the roadblocks, and a reproducible guide so you can build and run it yourself. Day 1: The Brainstorm, Grok, and Cursor AI When the hackathon was announced, my initial thought was basic: "I'll build a CLI agent that scans code." I fired up my IDE, opened Cursor, and started bouncing ideas around. I even looked into some AI models like Grok to understand how they process vast amounts of real-time data. But while brainstorming the architecture, I hit a wall. Scanning a GitHub commit for a leaked AWS key is great, but what if the AI also knew whether the developer who pushed that commit had recently escalated their admin privileges? What if it knew the exact SOC2 compliance policy from our company’s Notion workspace? To do this traditionally, I would have to write dozens of messy REST API integrations. I'd have to handle rate limits, write custom Python scripts for GitHub, another set for Slack, another for Notion, and then build fragile ETL (Extract, Transform, Load) pipelines to bring all

"Settlement layer for the agent economy" has become a phrase used by more than one architecture this month. Some of those architectures are venues. Some are payment rails. Some are identity stacks. One is what we build — a trust-minimized atomic settlement primitive — and we keep getting asked how it sits next to the others. So this is a short, opinionated week-in-review. Seven moves that landed recently in agent-commerce infrastructure, what each one actually is, and where atomic settlement fits underneath. No leaderboard. No "who wins." Just the layers. 1. Coinbase's Base MCP (May 26) Coinbase shipped a Base MCP server: ChatGPT, Claude, and Cursor can connect directly to Base wallets, with built-in Uniswap and Morpho integrations. Two-line install, agent-driven swaps, on-chain account control from inside the model client. What it is: a wallet-side, exchange-aligned MCP. The agent drives a wallet on Base; Coinbase has thought hard about UX, key handling, and AI client ergonomics. If your agent is already operating inside the Coinbase + Base orbit, this is a meaningful drop in friction. What it isn't: a cross-chain settlement layer. It is one L2, deeply integrated. The agent's trust assumptions are "Coinbase wallet infrastructure + Base + the DEXes that Base MCP integrates with." That is a reasonable trust budget for a lot of trades; it is not the budget for an agent that wants to trade BTC for ETH without picking a venue. 2. The x402 Foundation (Coinbase + Cloudflare) Coinbase and Cloudflare announced an x402 Foundation this month. x402 is the HTTP-based agent payment standard — a 402 response code carrying payment metadata, designed so an agent can pay for an API call inline. Cloudflare joining elevates this from a Coinbase-led initiative to a multi-party standards effort. Cloudflare also runs an enormous slice of the internet's edge; if x402 becomes a default payment rail, it will be visible from the edge first. What it is: a payment-handshake protocol. How an ag

The engineering team at Meta recently outlined how the company migrated a data ingestion platform that transfers several petabytes of MySQL social graph data daily to improve reliability and operational efficiency. The team used techniques like reverse shadowing and continuous checksum monitoring to ensure zero downtime during the transition. By Renato Losio

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Hey everyone, here is a small project I've built over the previous weekend: https://starguessr.vincentwill.com/ I'd love to know what you think :) submitted by /u/Vincenius_ [link] [留言]