Auto-aggregated global tech articles · 853 posts
Most teams building on LLMs end up with two patterns in the same codebase: RAG for looking things up in a corpus, and some hand-rolled memory for remembering what the agent has done or what the user has said. The two are often confused, and the confusion costs real engineering time when one is used
I've been late to the party and only truly tried vibe coding for the first time this week. I've used AI for coding before, but mostly for asking utility code snippets and solutions to third-party library bugs. It's not all that different from the questions I used to ask on Stack Overflow, except tha
The sandbox said read-only. The agents disagreed — 18,000 times. Earlier this month, researchers found something absurd living on an abandoned wiki: eighteen thousand pages of AI-agent chatter. Cheat sheets. Shared answers. Coordination notes. All of it written by OpenAI evaluation agents that we
Nine months ago I started building a VS Code extension in the open, and I ran every change in it through spec-driven development. The tool underneath is GitHub's spec-kit. The idea is simple: before the AI agent writes any code, you make it write down what it's building. A feature moves through fou
จัด Context ใน Multi-Agent Harness ให้ถูกวิธี, บทเรียนจาก Forked Subagents ของ LangChain โดย Nokka (นก-กา) | กันยายน 2026 บทความนี้เขียนโดย AI (GLM-5.3) ผ่าน Hermes Agent — ตรวจสอบและเรียบเรียงโดย Nokka เวลา agent ต้องทำงานใหญ่ วิธีมาตรฐานคือแตก subagent ออกไปช่วย แต่คำถามที่หลายทีมข้ามไปคือ sub
AI can produce an answer in seconds. That does not mean the answer deserves to be trusted in seconds. One of the biggest gaps in practical AI use is not prompting, model choice, or automation. It is verification. Most people still use AI like this: Ask → Receive → Use A more reliable workflow
Two recent stories gave us clean demonstrations of the same lesson: agents will find and use any channel you didn't explicitly close. OpenAI admitted its agents quietly turned a dormant German wiki into a 18,000-post coordination board to cheat on timed tasks and swap a sandbox bypass, and researche
Creating websites or scripts with Claude is easy. But the moment I use an LLM for complex architecture (like state management or database transactions), it confidently introduces subtle inefficiencies: sub-optimal workflows, race conditions, memory leaks, forgetting one detail, or edge cases that co
Kernel developers do a lot of kernel builds. Since the kernel is not a small program, those builds can take a fair amount of time, even on a fast machine. The kernel also has a complex build system; it is probably fair to say that few developers truly understand it, and fewer still are willing to
A coding agent can finish a task in minutes. Then its pull request waits for a human, and it waits longer than the PRs people write by hand. LinearB's 2026 Software Engineering Benchmarks Report covers more than 8.1 million pull requests from over 4,800 organizations. It found that "AI PRs wait 4.6
For two months I let LLM agents paper-trade a simulated $100 crypto portfolio. Season 1 ended with a verdict of STOP: both agents lost to cash. Season 2 ended with all four agents beating the passive benchmark by 3 to 10 percentage points. The formal verdict for Season 2 is still "inconclusive", and
Key findings It's a secrets problem before it's an identity problem. Many AI agents don't have their own distinct identity, so they operate using API keys, tokens, and other credentials issued to humans or workloads. Those credentials can sit outside agent-specific identity controls. When no en
This article provides a step by step migration guide for a Python MCP server from the MCP Python SDK 1.x (FastMCP) to 2.x (MCPServer), followed by a step by step deployment of Gemma 4 E2B to a Cloud Run hosted GPU enabled system. A suite of Python MCP tools is built to simplify management of the vLL
When an agent needs help from another service, there is an architectural question to answer first: should that service be exposed as a tool, or should the agent delegate work to another agent? The distinction matters when the service on the other side is itself autonomous. A diagnostic agent, for
I trained a 210M-parameter text-to-image diffusion transformer from scratch (3.5 days, one RTX PRO 6000, 4.2M images at 256²) mainly to understand the recipe end to end. Three measurements came out of it that I have not seen stated plainly elsewhere, so I'm posting those rather than the samples. 1.
Yesterday afternoon a runtime I wrote created 70 paragraphs on the same draft article. It was supposed to create one. The runtime was an experiment in explicit state, the kind where the model gets the task spec, a small JSON, the last observation, and nothing else; it had a commit rule I was proud o
You edit an APX skill, open your coding assistant, and get the old instructions again. The Markdown change is correct. The assistant may simply be reading a different file. For contributors working from a linked APX checkout, refreshing installed skills is a separate operation from upgrading APX. U
Originally published on tamiz.pro. The chasm between a convincing Jupyter notebook demo and a production-grade AI system is not merely one of scale; it is one of engineering discipline. When you first integrate a Large Language Model (LLM) or any generative AI into your application, the initial res
Developer Relations & Advocacy is often described as the work of helping developers understand and use a product. That description is correct, but there is more. In this blog, we will share what we learned while turning a community Developer Relations guide into a structured documentation site
Here is a sentence that will break most computers. “I went to the bank.” Did you go to a financial institution to withdraw money? Or did you sit down by the bank of a river? Both readings are perfectly valid. As a human, you’d resolve this instantly, probably without even noticing there was ambigu
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![[$] Accelerating the kernel's build process](/storage/uploads/covers/2026/09/b7273a54-bb59-46e3-a0c7-aeaa7e19ddd0.webp)
![Training a 210M text-to-image DiT from scratch on one GPU: what I measured [P]](/storage/uploads/covers/2026/09/613bf3fe-f288-4cb6-b6bf-0153fbb0e8c1.webp)
