Tired of AI 'magic' breaking your codebase? Stop 'vibe coding' and start building a hand-cranked, deterministic momentum engine with Git, local Python scripts, and safe clipboard handoffs. #Git# #AIEngineering# #TerminalPower#

There's a problem with test suites. Agents are trained with RL to reach the piece of cake by the shortest path forward. Which means they can reach the cake without ever solving the core logic of the issue. It happens like this, all the time: - you ask an agent to run a task - it starts looping - it hits an obstacle — an error, or something it can't do - instead of closing that sub-task, it finds an alternative that lets it move forward - the alternative quietly rewrites the test - the suite is green and the task isn't done A better way: make the agent verify its own work on the real system, as if it were the final user, on the UI. Falsificating a visual proof is harder than falsificate a coding test That's the whole idea behind the verification skill. Credit to @poteto, engineer on the Grok team at X We run it on aFFido. Here's how it works. One skill: verify-affido-ui. Playwright chromium, 1440×900, against vite on :5173. It fills the real signup form and keeps the real better-auth session cookie — the same one Caddy's forward_auth validates against Postgres before proxying anything. No mocks. No calling /api/* directly. No rows written by hand. If a person can't do it with a mouse, the agent can't do it either. Then one prompt, the entire path: browser → vite :5173 → caddy :18080 → sandbox-manager /resolve → per-user sandbox container Every step has to leave three files on disk: · 03-chat-reply.png — the screenshot · 03-chat-reply.txt — that page's visible text, so the evidence is greppable · facts-chat.json — step timings, plus three arrays that must be empty: httpErrors, consoleErrors, networkFailures And one rule it can't skip: a screenshot it did not open is not evidence. It reads every PNG, checks it against the .txt twin and the JSON. If they disagree, the screenshot wins. The mentality is: verify do not trust 👇 #AIAgents# #Verification# #Playwright# #Testing# #AIEngineering#

🔍 A DeepSeek Harness Plugin That Lets AI Explain the Agent's Own Trace Less than two weeks after DeepSeek Harness entered developer preview, its plugin ecosystem is already taking shape. Zhihu contributor 刘琦 built DSH Trace Insight, a focused plugin for one of the most important but overlooked parts of Agent engineering: understanding what an Agent actually did. 1️⃣ DeepSeek Harness exposes the process Many Agent harnesses behave like black boxes. Users provide a task, wait, and eventually receive an answer. DeepSeek Harness is different. Its trace records tool calls, execution steps, failures, retries, and other intermediate activity. This makes the Agent more transparent, but the raw trace is stored as a large JSONL file. Even with filtering, it is difficult for a human to read and reconstruct the full process. The author's idea was straightforward: If the trace is too complicated for humans, let another AI interpret it. 2️⃣ Turn raw traces into readable analysis DSH Trace Insight adds a side panel that asks an AI model to explain the running trace. It can summarize: 🔹 What the Agent is doing 🔹 Which methods and tools it used 🔹 Where errors or retries occurred 🔹 Whether any risky actions appeared 🔹 What lessons can be extracted from the run Instead of waiting beside an opaque progress indicator, users can inspect how the task is progressing, whether the approach is working, and how risky the current behavior looks. The goal is not to add another capability to the executing Agent. It is to add an interpretability layer around the Agent's behavior. 3️⃣ Use two models for cross-checking When a suspicious step appears, the plugin can send the same trace to two different AI models and compare their analyses. This is useful because trace interpretation is still a model-generated judgment. A second model can expose disagreements, missed risks, or different readings of the same tool call. The plugin can also organize detected issues into a compact list for manual review. That creates a useful three-layer workflow: Agent execution → AI trace analysis → human review It is a lightweight approach to Agent observability without requiring users to inspect thousands of raw log lines. 4️⃣ Installation is intentionally simple The plugin is open source under the MIT license: Users can ask their Agent to install it directly with: Please install this DSH plugin: The current version is designed for the native DeepSeek Harness Web UI. Using it inside third-party desktop wrappers may require additional development, since those clients may package or modify the original Web UI differently. 5️⃣ DeepSeek Harness can become a model worker behind Codex The author also suggests an interesting setup for people who do not use DeepSeek Harness as their primary Agent interface. Open the native DSH Web UI inside Codex's browser. Codex remains the main harness, while it operates DeepSeek Harness and the models connected to it. This creates a layered workflow: 🔹 Codex handles planning and orchestration. 🔹 Lower-cost non-GPT models inside DSH perform lightweight tasks or code inspection. 🔹 DSH Trace Insight exposes how those models executed the work. 🔹 Codex can discuss the results with DSH across multiple rounds, then send the final conclusion to another strong model for an additional review. Compared with assigning every subtask to an expensive model, this setup can reduce cost. Compared with calling another CLI tool blindly, it provides much better visibility into execution. ✅ The real value is observability DSH Trace Insight does one thing: it translates an Agent's raw execution history into something humans can understand. That simplicity is its strength. As Agents begin running longer tasks with more tools and greater autonomy, the important question is no longer just whether they produced the correct answer. We also need to know how they reached it, what failed along the way, and whether they crossed any risky boundaries. 🔗 Full Reading: #DeepSeek# #DeepSeekHarness# #AIAgents# #AgentObservability# #OpenSourceAI# #AIEngineering#

TL;DR: Same Claude Opus, no harness → $9 outcome in 20 minutes. Same model, full harness → $200 working product in 6 hours. This open-source curriculum teaches why — systematically. learn-harness-engineering "The model is smart. The harness makes it reliable." — 14 lectures and 8 projects covering harness engineering for AI coding agents. ⭐️ 13.7k Key points 🔧 5 subsystems form the harness skeleton · Instructions: AGENTS.md and structured guidance make the repo the single source of truth · State: claude-progress.md + git history enable multi-session continuity · Verification: tests, lint, E2E pipelines prevent false completion claims · Scope: explicit completion criteria enforce one-feature-at-a-time progress · Session Lifecycle: init → implement → verify → commit → handoff as a structured protocol 📚 Progressive learning path From fundamentals (seeing the problem, structuring repos) to advanced patterns (automated loops, graph-based workflows, Human-in-the-Loop). A shared Electron knowledge-base app provides hands-on practice at every stage. ⚡ Instantly applicable to existing projects Copy AGENTS.md, and feature_list.json from the template library into any project root for immediate structured context and scope constraints. The zero-dependency diagnoses harness quality on the spot. 🌐 Current frontier harness designs included Breakdowns of Pi, Claude Code, Codex, and DeepSeek harness architectures (added August 2026), plus loop and graph engineering lectures with templates (July 2026) — the course continuously reflects real-world practice. The shift from "prompt the agent better" to "design the environment for reliability" is what 13.7k engineers are endorsing here. #CodingAgent# #AIEngineering#

✍️ Now that AI can write hundreds of syntactically valid lines per second, the developer's job has shifted from "writing" to "reading and verifying." That shift changes what we should want from a programming language. Why Go is an Ideal Language for AI-Assisted Software Engineering 🔍 Overview Google's Go team makes the case for Go as the ideal language for the AI-assisted development era. As the bottleneck moves from code generation to review and verification, the language properties that matter most have flipped — readability and maintainability win over writability and expressiveness. ⚠️ The Problems Being Solved ・LLMs frequently hallucinate type mismatches and non-existent properties ・Dynamically typed languages (e.g. Python) only catch these errors at runtime ・AI-generated code often pulls in stale packages or vulnerable dependencies ・Repeated refactoring passes degrade accuracy (~95% first-pass, declining) 🛠 How Go Addresses Each ・gofmt enforces uniform formatting — AI output is always syntactically predictable ・Static typing + fast compilation: rejects errors instantly, orders of magnitude faster than Java, C#, or Rust ・govulncheck: low-noise vulnerability scanning targeted only at invoked symbols ・Native fuzz testing: continuous discovery of boundary-condition bugs ・15 years of strict backward compatibility: Go 1.0 code runs unchanged today 📌 The Counterintuitive Conclusion "As developers write less code, language choice becomes more critical." Absorbing AI's high-velocity output safely requires deterministic guardrails — and Go was designed for exactly that. #Golang# #AIEngineering#

🚀 An ebook that takes FastAPI from your first endpoint all the way to production-scale AI systems — going deep on LLM/RAG serving, with interview questions at the end of every chapter. Title: FastAPI for AI Engineers: From First Endpoint to Production-Scale AI Systems URL: 🚀 Overview A practical guide (First Edition, 2026, by AI Engineering Insider) for AI engineers serving ML models and LLM/RAG systems in production with Python. It spans 10 chapters and 100 interview questions, woven with real-incident case studies and cost-model sidebars. ❓ Challenges Solved ・Building a model is one skill; serving it safely as a scalable production API is another ・LLM/RAG serving has its own hard parts — streaming, guardrails, cost control The book reframes FastAPI as "the de facto serving layer for AI and ML systems." 💡 Structure & Tech Covered ・Foundations: ASGI/WSGI, Uvicorn, OpenAPI, and Pydantic v2 schema separation and validation ・Implementation: idempotency, meaningful status codes, pagination, and the Router→Service→Repository clean architecture ・DB/Security: SQLAlchemy/SQLModel/Alembic, N+1, pool sizing, JWT, BOLA defense, OWASP API Top 10 ・Async: "never block the event loop," def vs async def, and httpx retries/circuit breakers 🎯 The Core (Ch.9: AI/RAG/LLM) ・Load model weights once in lifespan; offload CPU inference to a thread ・An LLM gateway centralizing auth, prompts, guardrails, and cost metering, with SSE token streaming ・Build RAG with embeddings + a vector DB (start with pgvector); validate output with Pydantic, then retry on failure ・Enforce max_tokens as a "spending limit" via the type system 📊 Highlights ・Practice-first, learning from real incidents (Netflix, Stripe, GitLab, Optus, Air Canada) ・Ch.10 covers Gunicorn+Uvicorn, K8s liveness/readiness, the three pillars of observability (p99 vs p50), and SLO-based alerting #FastAPI# #AIEngineering#

Anthropic AI engineer just showed how to give AI agents real memory in 4 steps - and it changes everything in 28 minutes he shows exactly how agents can remember across sessions, completely free worth more than any $500 AI engineering course here's what he covers: • why agents forget everything between sessions • memory stores - agents read, write across sessions • dreaming - agents that improve their own memory • 95% cache hit rate, so it stays cheap most people are still copy-pasting context into every new chat - while the people who figured this out are building agents that get smarter every single night watch full video then read article below

Quick lookback on technical AI themes of 2026: (dataset: daily ainews title stories) Jan: - Ralph Loops - RLMs - Filesystems are all you need - X LLM recsys open sourced - X Grok Imagine #1# Video model - Anthropic blocked OpenCode / OpenClaw - MCP Apps - Moltbook - Apple picks Gemini for new Siri - Notable models: Kimi K2.5 - Fast inference: OAI x Cerebras partnership - Big raises: xAI @ $230B + SpaceX, OpenEvidence @ $12B Feb: - Codex App launch - Claude Code 1 year anniversary - Context Graphs - Fast Inference: Taalas 17k tok/s/user Llama 3 8B - Anthropic vs Deepseek/Moonshot/Minimax distillation accusation - Notable models: Opus/Sonnet 4.6, 5.3 Codex and Codex Spark, Qwen-Image 2.0, Seedance 2.0, GLM-5, Gemini 3.1 Pro and Deep Think, Nano Banana 2 - Big raises: OpenAI @ $840B, Anthropic @ $380B, ElevenLabs @ $11B, Cerebras @ $23B Mar: - T3 Code - Autoresearch - Claude Code source leak - Claude Cowork Dispatch - OpenAI buys Astral - Cloudflare-Next.js "Slop Fork" - Meta buys Dreamer - NVIDIA GTC: OpenClaw, Vera CPU, 1T sales backlog, "The Inference Inflection" - ARC-AGI 3 introduced, SOTA models are 1% - Notable models: GPT 5.4 unifies chat and codex, Gemini 3.1 Flash-Lite/Live, Nemotron 3 Super, Composer 2, MiniMax 2.7, Arcee’s Trinity-Large-Thinking - Big Raises: Yann LeCun AMI Labs $1B seed Apr: - Claude Design - OpenAI Symphony & Harness Engineering discourse - @LilianWeng writeup later - OpenAI and Microsoft break up - Tokenmaxxing fears - Codex Superapp - AIE Europe, AIEi Miami - Hermes Agent gains traction w/ Manim skill - Cyber: Anthropic Project Glasswing - Notable models: Claude Mythos "Capybara", Opus 4.7, Muse Spark, Gemma 4, Kimi K2.6, GPT-Image-2, GPT 5.5 "Spud", DeepSeek v4 Pro/Flash Base/Instruct on Huawei Ascend, Poolside Laguna XS.2, Grok 4.3 - Big Raises: not much but Anthropic $19B->$30B ARR in one month... May: - Cyber: OpenAI Daybreak, Tanstack Mini-Shai Hulud - FDE: OpenAI DeployCo, Ode by Anthropic - OpenAI solve Erdos planar unit distance problem - OpenAI deprecates finetuning - "How to land a job at a frontier lab" - Anthropic rents Colossus I from SpaceX for $5B/yr - Thinking Machines Interaction Models (still underrated!!!) - Notable models: GPT-Realtime-2/Translate/Whisper, Gemini 3.5 Flash, Omni, Antigravity 2.0, Opus 4.8, Cosmos 3, Minimax M3 - Big Raises: Fireworks @ $15B, Baseten @ $11B, Cognition @ $26B, Cerebras $60B IPO, Anthropic @ $965B Jun: - Claude Tag - OpenAI Jalapeno - "The Untrainable" - @saranormous - Loopcraft: The Art of Stacking Loops - Metaharnesses discourse? Flue v Eve - Zai Prof @jietang predicts Open Fable by Dec - "SpaceX is a $28B/yr neocloud" - @jaminball - Anthropic-Fable White House issue (resolved) - Notable models: GPT 5.6 Sol/Terra/Luna, Fable 5, Sonnet 5, MAI-Thinking-1 and MAI Family, Nemotron 3 Ultra, GLM 5.2, Reve 2/ Ideogram 4 - Big Raises: SpaceXai buys Cursor for $60B, Supabase @ $10B Jul: - AIE World's Fair 3 - OpenCode 2.0 - Cyber: OpenAI-HuggingFace incident - Chinese Model ban discourse - GPT 5.6 Luna 80% price cut - Notable models: Grok 4.5, Opus 5, Muse Image, Thinky Inkling, Kimi K3, BFL FLUX 3, DeepSeek-v4-Flash-0731 - Big Raises: Aug: - EU AI text watermarking - Microduck - Megakernels - Memory prices up 500% in 12 months - How to steal a Reasoning Trace - Grok @bot! - OpenAI shuts off Cursor - Fast Inference: AMD buys Taalas - @andrewng stars AI Engineering series - Hot Chips: Jalapeno, Cerebrase CS-5, Groq 3, Apple M6 - Demis steps down from CEO, & Dean et al leave - Notable models: Qwen 3.8 Max, Muse Spark 1.2, Muse Glimmer, Grok 4.6, Gemini 3.7 Flash, GLM 5.3, @Fal H3 Max Live - Big Raises: Stripe buys OpenRouter $7B, Poolside $12B reverse-execuhire to NVIDIA, NVIDIA buys HuggingFace for $13B Sep: - AIEi Paris - OpenAI Navier-Stokes singularity - collusion . wiki - Yegge shuts down Gas Town - AEF-1 standard for Third Party Evaluators - Meta Muse App launch. Also new glasses and Charm - OpenAI blocks Cursor - OpenAI Dots - Opus 5.5 explainer videos - Pi 1.0 & Pi Durable - Notable Models: Fable 5.1, Mythos 5.1, Opus 5.5, GPT-6 Astra, GPT Image 2.5, DeepSeek v4.1-Flash, @typesafeai Jev, MiMo-v2.6-Pro new Open SOTA, GPT 6.1 Sol & Ultrafast & Decisions API, Gemini 4 Argon - Big Raises: Cognition @ $48B, Mistral @ $24B, AMD buys World Labs for $8.2B Oct to date: - AIE NYC - @dhh stops using Rails - ArtCraft clones Adobe suite - Interpretability vs Polysemanticity - OpenAI Quasi-Riemann-Hypothesis & 722 Math - Notable Models: Reflection Beam, Claude Haiku 5.5, Muse Wonder & Wonder Full reach Opus 5.5 tier - Big Raises: TypeSafe $7.5B 3 weeks after launch, Modal @ $16B (this was done for research purposes for the keynote in Nov, just sharing notes in case helpfuil)