Auto-aggregated global tech articles · 853 posts
Atlassian reports its Rovo Dev AI reviewer cut PR cycle time by up to 45% internally and 32% for customers (primary source, Jan 2026). That figure gets quoted as proof that AI review saves review time. Read what the reviewer is described as doing and the number is mostly about routing. The post say
Specific Labs dropped Real-SWE, an enterprise-code SWE benchmark, and the leaderboard is a great study in why you should never read "Claude Code" or "Codex CLI" as a model name. Same harness, two different brains: GPT-6 Astra on Codex CLI: 33.8% resolution GPT-5.6 Sol on Codex CLI: 16.2% resoluti
Real-SWE ran frontier models against licensed, private enterprise codebases (billing, tax, multi-service work) and one number jumped out at me: rollout duration barely moves resolution. 71.4% of rollouts that finished in under 10 minutes FAILED. 73.4% of rollouts that ran 10 minutes or longer also
Agent loops burn tokens and clock cycles on repeated work. The same LLM prompt fires twice, the same tool call fetches identical data, and session state gets reconstructed from scratch on every request. Agent-cache solves this with a three-tier caching architecture backed by Valkey or Redis, putting
❔Contexte Le #HackAVP - le premier hackathon dédié aux AVPs de l'OPT-NC - a été lancé cette semaine : 🏆 #HackAVP, premier hackathon dédié à l'emploi dans la fonction publique en NCL
ราคา cache hit 0.003 ดอลลาร์ ที่เปลี่ยนวิธีเทียบโมเดลทั้งตลาด โดย Nokka (นก-กา) | 13 กันยายน 2026 บทความนี้เขียนโดย AI (โมเดล deepseek-v4.1-flash ของผู้ให้บริการ ollama-cloud) ผ่าน Hermes Agent จาก Nous Research ตรวจสอบและเรียบเรียงโดย Nokka เวลาคนเทียบราคาโมเดล สิ่งที่ดูกันคือ ราคาต่อโทเคน แต่ม
TLDR: AI has made prototypes, POCs and MVPs cheap — a plausible demo in days. What has not gotten cheaper is everything after: reliability, adoption, governance, cost-to-serve. Teams that fund projects now get more demos. Teams that fund products — with stable ownership and health metrics — get more
Every trip report on my travel site goes through an LLM before readers see it. The most important line in that prompt is not about catching bad content. It is this one, verbatim: - Negative reviews are ALWAYS allowed. A harsh critique of a hotel/destination is legitimate content. "Bad revie
The first prototype of Loose End had a string, three pegs and four bells. It also had arrow-key controls that ignored short presses. A coordinating agent found the input problem and sent it back to the builder before the game was finished. That repair is part of GPT-6 Astra's first Choice batch. I
I've probably worn out my welcome by now with this old-school habit of mine — explaining DDD or SDD, or pushing for TDD (which, in practice, almost nobody actually uses, except people with... particular tastes, or the ones who just wanted to try something backwards 😏), and plenty more besides. I'm
Cross-Chain Bridge Risk Assessment: Robinhood Target Protocol: Robinhood (TVL: $14270.6M) Cross‑Chain Bridge Risk Assessment – Robinhood TVL: ≈ $14.27 B (Ethereum + L2) Date: 13 Sept 2026 Prepared by: Senior DeFi Security Researcher – Confidential 1. Executive Summary
I went looking for a straight answer to "what will this cost per month" and found that almost every comparison table gets it wrong the moment prompts get long. Not slightly wrong. Double. The threshold nobody shows you Gemini 3.1 Pro is $2 per million input tokens, $12 per million output
"The coordination of the builders is not direct. It is the work already done that directs and triggers the work that follows." — Pierre-Paul Grassé, describing termites, 1959 1. The Loop You have seen this. Every agent framework does it :) > create_issue x 422 validation_error
Most "AI chatbot" projects stop at a demo: a widget, an OpenAI/Gemini call, a canned prompt. Getting one into production for real businesses — across voice calls, web chat, and WhatsApp, in two languages, without hallucinating — is a different problem. Here's how I approached it. The Core Challenge
Hello, I'm Rijul, and I'm building LiveReview — a blast-radius aware AI code review built for your business-critical systems. Star us to help devs discover the project, give it a try, and share your feedback to help improve the product. We see the pace of AI development speeding up. One way to v
Eliminating Terminal Bloat: Meet RTK (Rust Token Killer) As developers increasingly delegate terminal tasks, testing, and debugging to AI coding agents (such as Claude Code, Cursor, Codex, and OpenHands), context window saturation has become a major engineering bottleneck. Standard developer too
Damn, I like standards! I'm so happy to live in a world where we can finally use USB-C for almost everything... Oh, the article is not about USB-C, but about JSON Schema finally getting a "Standard" in 2026! Well, I'm kidding here... JSON Schema is a standard itself, which has existed for a long ti
You sit down Saturday with one failing test file. The stack trace is ugly and long. You want a tiny helper that groups those failures. Ninety minutes later you are still retrying a model. The helper never became a demo. The weekend dissolved into another chat. This log cuts that loop. You freeze t
This article provides a step by step update guide for a Python MCP server that drives Google Nano Banana 2 Lite (gemini-3.1-flash-lite-image) through the Gemini Interactions API. Two dependency lines moved underneath it since it was first published: the MCP Python SDK went to 2.x, and the Interactio
MIT ใช้เวลา 5 เดือนสรุปว่า AI กำลังกัดกร่อนการศึกษาที่มันสร้างเอง โดย Nokka (นก-กา) | 13 กันยายน 2026 บทความนี้เขียนโดย AI (โมเดล deepseek-v4.1-flash ของผู้ให้บริการ ollama-cloud) ผ่าน Hermes Agent จาก Nous Research ตรวจสอบและเรียบเรียงโดย Nokka มีข้อสังเกตหนึ่งที่ผมคิดว่าสำคัญก่อนอ่านรายงานฉบับ
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