SHIP

Gemma 4 12B runs on 16GB Macs at Q4, has native function calling, 262K context, and nearly matches the 26B MoE model on standard benchmarks.

MIT-licensed GLM-5.2 drops into Claude Code with one env var change. Trails Opus 4.8 by 1% on long-horizon coding tasks.

400,000 sessions. 235,000 users. 7 months of data. Here's what Anthropic's largest-ever study of coding agent usage actually reveals.

A developer's decision framework for local LLM hardware. Model size determines everything. 7B needs 8GB. 70B needs 128GB unified memory. Here is what each tier costs and how fast it runs.

Dense 8B models beat 120B MoE models on agent workflows. Ollama beats LM Studio on the same weights. BFCL V4 confirms: tool-calling accuracy drops from 85% (API) to 30-50% (7-9B local).

Benchmarks tell you what's technically capable. Production adoption tells you what actually works at scale. These are two different lists. Here's the 2026 open-source landscape ranked for real workloads.

Apple announced Core AI for on-device LLMs, Xcode 27 with multi-model agentic coding, and free AI for indie developers. Three announcements that change the AI agent landscape.

Kimi K2.7 Code is the first open-source model I'd actually use in an agent loop. It drops into Claude Code's CLI with one env var change. It scores within striking distance of Fable 5 on coding benchmarks at half the price. And the weights are open.

MiniMax M3 is the first open-weights model that doesn't make you choose. Frontier coding? 59% on SWE-Bench Pro (beats GPT-5.5). 1M context? Sparse attention makes it affordable. Multimodal? Native, not bolted on. At $0.60/M tokens, it's 12x cheaper than GPT-5.5.

OpenAI is acquiring Ona to give Codex persistent, secure cloud environments. Codex is no longer just a coding assistant. it's an agent platform. Here's what changes.

Your AI agent just scaffolded a project from 2020. It saw exit code 0, files appeared, and moved on. Here's why npm silently downgrades packages and what to do about it.

Sam Wilkinson's satirical HN post extrapolates Anthropic's naming scheme into absurd territory. Aphorism, Marginalia, Diatribe, Terms of Service. But the real names tell a story about strategy. Here's every one ranked.

npm v12 ships in July 2026 with three security-focused breaking changes to npm install. Staged publishing, stricter install scripts, and stronger package.json validation. Here's what breaks and how to fix it.

I launched Agentic Up as a solo AI agent studio in Bengaluru and tried 5 pricing models. Some made money. Some lost money. One actually became a sustainable business.

Three pricing models explained with AI-specific examples, scoping traps to avoid, and a proven method for raising prices over time.

Pricing is the hardest part of selling AI agent services. Here's exactly how I price. the numbers, the reasoning, and the mistakes that led to this model.

The AI job market in Bengaluru is real but noisy. Here's what companies are actually paying, what skills command premium, and how to stand out as a developer.

I've shipped 3 production support agents for startups in Bengaluru. Here's the architecture, the code, and the hard-won lessons. from RAG to escalation to cost per conversation.

There are four real paths to making money with AI as a solo developer. I've tried all of them. Here's what actually generates revenue, and what's a waste of time.

Starting an AI development business from India is different from starting one in SF or London. Different constraints, different opportunities, different mistakes. Here's what I learned.

एआई से पैसे कमाने के चार असली रास्ते हैं एक सोलो डेवलपर के लिए। मैंने सब आज़माए हैं। यह है जो वास्तव में राजस्व उत्पन्न करता है, और क्या समय की बर्बादी है।

बेंगलुरु में एआई नौकरी बाजार असली है लेकिन शोर भी बहुत है। कंपनियां वास्तव में कितना भुगतान कर रही हैं, कौन से कौशल प्रीमियम कमाते हैं, और एक डेवलपर के तौर पर कैसे अलग दिखें।