Auto-aggregated global tech articles · 853 posts
Episode 3 of the AI-Augmented Parallel SDLC series. The pillar article laid out the blueprint: discover once, run three workstreams in parallel, converge at one gate. Episode 1 put numbers on what the handoff chain costs you. Episode 2 got rid of the handoff at intake. This one covers the question e
Traditional RAG is what every "ChatGPT for your docs" tutorial builds: embed the question, fetch top-k chunks, stuff them into a prompt, return the answer. It works beautifully for ~75% of the questions you'd ask a support assistant. Then someone asks "My conversion rate dropped 18% last week — chec
TL;DR — I collected 558 AGENTS.md files from public repos and labeled each one against a 9-category taxonomy with a rule-based classifier (no LLM in the loop, so it is auditable and recomputable). Then I blind-labeled held-out samples and compared: 92% precision / 70% recall on 55 English files, 88%
Suppose I create two machine learning models suppose tree and neural network for a task let's suppose regression problem, now suppose I am sending both of this paper to two different journals, now the thing is the baseline models I need to only run once because I have reported same baseline in both
주말에 Migrate agentic workloads to Amazon Bedrock AgentCore 글을 읽었는데, 문장 하나가 눈에 박혔습니다. "노트북에서 돌아가는 에이전트는 프로덕션 에이전트가 아니다." 정확히 지난 분기에 겪은 상황이었습니다. LangGraph로 만든 고객 지원 에이전트 데모는 잘 돌아갔는데, 실사용자가 붙기 시작하니 세션 격리, 체크포인트 지속성, 툴 인증 같은 것들이 전부 우리 코드가 되어버렸거든요. Amazon Bedrock AgentCore는 이 "에이전트 추론과 상관없는 운영 부담"을 서비스 단
They can inspect information, call tools, evaluate results, and decide what to do next. That loop is what makes an agent more capable than a simple chatbot. But the same mechanism can create a serious engineering problem. An agent can get stuck repeating the same action without making meaningful p
Hi everyone, Inspired by tools like Chessvision.ai, I wanted to take a different architectural approach and build a browser extension (ChessInsights AI) that performs chessboard detection and piece recognition 100% client-side using local inference—with zero image data ever leaving the user's machin
When a Python microservice misbehaves in production, you reach for logs and distributed traces. When an LLM-powered application misbehaves, most teams are flying blind. Tokens leak, latency spikes, costs explode — and all you have is a user complaint. That gap is what AI observability is designed to
I set out to score how much of a job ad is a real commitment. The number I got is not the interesting part. The interesting part is that I agreed with myself only 75% of the time until I wrote the rule down. "Competitive salary." "Fast-paced environment." "You will own your work." Delete those sent
How I Fine-Tuned a 7B LLM with LoRA and Unsloth When I was working on Khatta-ka-LLM, two technologies became especially important: LoRA + Unsloth If you've heard these names but aren't sure what they actually do, here's a simple explanation. What is LoRA? LoRA stands for Low-Rank Adaptation. I
If the name lencx sounds familiar, it's probably because of his last project — the ChatGPT desktop client, the open-source app that put ChatGPT on thousands of desktops. His new project is Minke (637 stars, Apache-2.0): a local-first desktop agent workspace powered by DeepSeek Harness. Th
AI agent loops can produce an output, run some checks and move on without establishing that the result actually satisfies the task. The problem gets worse when the same generator writes the work, checks it and decides when it is done. Say you ask an agent to modify an API and add tests. It writes t
Oracle AI Database 26ai answers this with a new feature called Oracle Deep Data Security , or “Deep Sec.” Deep Sec is a database-enforced authorization framework: instead of hoping application code, middleware, or an AI agent behaves correctly, you declare exactly which rows, columns, and even indiv
When I add an AI step to a building workflow, I treat it like an untrusted dependency. It can produce useful candidates quickly, but every output needs a source, a scope, and an owner. That approach fits the current US policy environment: federal agencies are publishing governance and procurement gu
Spotify's app is great at finding music and mediocre at ordering it. If you've ever built a two-hour playlist for an event and watched it lurch from a slow ballad straight into peak-time energy because the app has no idea what a "set" is, you know the gap. So I built a small open-source CLI to fill
Fine-tuning is the process of further training a language model on new data so it learns new information, behaviors, or style — updating the model's own weights, rather than just showing it information at the moment you ask a question. There are two common ways to get an LLM to work with new inform
OpenAI has publicly backed a more deliberate approach to frontier AI development and said it will give independent evaluators employee-like access to its frontier work. The commitment, made by CEO Sam Altman on September 12, 2026, aligns OpenAI with Anthropic CEO Dario Amodei's call to "pace the fro
Gemini 3.6 Flash: Cut Agent Token Costs 65% Your AI agent works until it reaches the expensive part: reading a large codebase, retrying failed tool calls, carrying state across dozens of steps, and producing a long implementation plan. For a solo developer or small team, the issue is rarely whethe
💡 𝒫ℯ𝓇𝓈ℴ𝓃𝒶𝓁 𝒮ℯ𝓇𝒾ℯ𝓈: ℳ𝓎𝓉𝒽𝒾𝒸 ℱℴ𝓇℃ℯ 𝒟ℯ𝓋ℯ𝓁ℴ𝓅𝓂ℯ𝓃𝓉 ℒℴℊ 𝒯𝒽𝒾𝓈 𝓅ℴ𝓈𝓉 𝒾𝓈 𝓂𝓎 𝓈𝓊𝒷𝓂𝒾𝓈𝓈𝒾ℴ𝓃 𝒻ℴ𝓇 𝓉𝒽ℯ ℳℒℋ × 𝒟ℯ𝓥 𝒞𝒽𝒶𝓁𝓁ℯ𝓃ℊℯ (✿◠‿◠)⭐ (っ◔◡◔)っ 💖 🌸 "Bring your imagination to life, one stat block and backstory at a time." Have you ever spent hours trying to dream up a compelling tabletop RPG character, only to get stu
Sampling Parameters as Substances: An Honest Analogy A strange question that works: why does cannabis seem to make people more creative? It doesn't make anyone smarter — it changes filtering. The mind lets through associations it would normally reject. Distant ideas reach awareness more easily. T
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