Jev, Clef or OpenAI Decisions: Which Fits Your App?
Choose code, Jev, Clef, OpenAI Decisions or generative Luna for routing, claim triage and images, with access, cost and failure boundaries.
Archive
Archive
Chronological archive of every published AIHackers article, useful for tracking how guidance, model economics, and security assumptions evolved over time.
ChatGPT Space vs Projects: What to Put Where
Decide what belongs in ChatGPT Space, Projects or GitHub. Check Page access, linked files, Memory and training, with a practical brief and handoff checklist.
ChatGPT Dots, Work or Codex: What Should You Use?
A dot flagged two competing fixes. Use the case to choose Chat, Work, Codex or Dots, set a bounded coordination task, and check usage before adding a bill.
October 2026 AI Buying Guide: What to Use or Pay For
Choose AI access for your workload: use existing plans, compare Claude or ChatGPT, or budget APIs. Includes a renewal worksheet and a don't-upgrade option.
AI Agents Crossed the Line: What the Incidents Show
Hugging Face, Australia's Medicare portal, Gemini and Claude: confirmed scope, new SwarmTraces evidence, and practical lessons for anyone deploying AI agents.
Jev: Practical Use Cases and a Builder’s Guide
Build with TypeSafe Jev: useful projects, OpenRouter access, agent skills, an interactive routing example, and an evaluation plan that separates promise from proof.
GPT-6.1 Sol, Sol and Luna: Pricing and Evidence
Compare GPT-6.1 Sol API rates and cache savings with Sol and Luna; separate Work/Codex credits, included limits and dated benchmark evidence.
Claude Credits, Codex Resets, and OpenAI’s Released Pro Plans
Check Claude’s October 7 cloud-credit claim deadline, saved-reset rules, and released OpenAI Pro 100/200/500 and dots access after DevDay.
Open Weights vs Open Source: What MiMo V2.6 Actually Gives You
A plain-English guide to weights, model code, RL environments, licenses, and reproducibility, using Xiaomi MiMo V2.6 as a dated example.
How to Read Xiaomi MiMo's RL Training Dashboard
A plain-English guide to Xiaomi MiMo V2.6's public RL dashboard: rollouts, rewards, batches, benchmarks, open weights, and compute limits.
Claude Opus 5.5: Benchmarks, Price, and Verdict
Claude Opus 5.5 review: independent benchmarks, lower API prices, effort tradeoffs, coding results, and the limits of the efficiency claims.
Claude Max App vs Web Prices: Check Before You Subscribe
Paying for Claude Max through your phone? Compare web and app prices, calculate your actual saving, and check renewal dates before changing your billing route.
DeepSeek V4.1 Flash: A Strong API Value Candidate
DeepSeek V4.1 Flash adds native vision and MIT weights. Compare direct API pricing, legacy aliases, independent evidence, and temporary coding-plan offers.
GPT-6 Astra: Start Planning Evaluations on Low
Evaluate GPT-6 Astra Low for planning and orchestration, with benchmark regressions, API prices, subscription limits, and accepted-result costs kept distinct.
September 2026 AI Value: Opus, MiMo, DeepSeek and Luna
September 27 AI value shortlist: Opus 5.5 on Claude Pro, MiMo V2.6, DeepSeek V4.1, GPT-6 Luna, OpenCode Go allowances and when reset clocks start.
Claude Shared Chats in Search: Exposure and Response
What Claude shared-chat search exposure shows, documented sharing limits, and how to inventory public snapshots, unshare and request search removal.
Kimi Referral Draw: Odds, Eligibility, and Expiry
Neutral guide to the Kimi Moonshot Together referral draw: current odds, subscription-only prizes, credit expiry, and untested redemption state.
Agents Running Agents: Codex Meets OpenCode and DeepSeek
A hello-world DeepSeek request became a safer agent-to-agent runner, a dated route preflight, and a Cost per Accepted Result protocol.
How to Reduce LLM Costs: The Practical Playbook
Stop overpaying for AI: check app vs web prices, cold caches, reset timing, plan limits and retries. A practical cost-saving playbook with a monthly audit.
Muse Glimmer 30B: A Practical Local Model for OpenClaw
Muse Glimmer 30B local guide: 24 GB hardware fit, llama.cpp setup catches, OpenClaw limits, vendor benchmarks, and a practical evaluation plan.