Opus 5.5 defaults to medium effort, and Claude Code 2.1.317 changed subagent effort on Nov 3. The settings fix, plus a script to test it yourself.
The text model you want is Gemini 3.8 Flash: 19% on Terminal-Bench 4.0, price doubling Jan 1, and 2x Terra's cost per solved task.
libghostty-vt 1.0 has a stable C ABI, builds to under 1 MB of WASM, and needs only libc. What it does, what it leaves to you, and a C example.
OpenAI's own report: Astra hit ~10% of rubric items on safety tasks vs ~16% otherwise, and its monitors didn't flag it. Six checks that catch it.
That 290MB is one cropped Task Manager screenshot, so measure your own Discord client, change the settings worth changing and skip Ripcord.
OpenAI's calculator multiplies gpt-image-2.6 low quality by 4.2, and 2.6 has no Batch API. Price 2.5 before gpt-image-1-mini goes away on December 1.
Anthropic's block needs 2.1.317, stable was on 2.1.311 on Nov 6, and it skips WebFetch. Grep your logs, then add this PreToolUse hook.
DuckLake writes took 3+ minutes because over 99% of catalog stats belonged to deleted tables. Two SQL queries show whether yours is next.
Devs who learned with AI scored 17 points lower, worst on debugging. The ones who kept pasting errors back into the AI did worse still: about 24%.
Carbon's hallucination rate is 11% to Opus 5.5's 50%, and GPT-6.1 Sol scores 55 for $1.27 a task. Here's which one to use for what.
CPython's JIT leaves main early in the 3.16 cycle. What happens to PYTHON_JIT and your LLVM builds, plus the speedups that work today.
Terra costs $0.40 a task on Artificial Analysis vs $5.98 for Opus 5.5 but scores 48 to 58, and at max effort GPT-6.1 Sol is the better buy.
uv check also type-checks PEP 723 scripts against their own dependencies, and one uv add --dev ty stops your laptop and CI drifting apart.
It's private preview with no published price. Here's the OpenAI model you can call on Vertex AI today, and what GPT-6 costs on Azure and OpenAI.
Old deepseek-v4-flash calls are already billed at the new rate. Cost per task vs Haiku 5.5 and GPT-6 Luna, and where DeepSeek still wins.
The NSA's mystery P-256 seed is lrand48 output you can reproduce in 11 lines of C. Plus: should you still pick Ed25519 for SSH, JWT and TLS?
Eight to ten features were reverted, Postgres 14 hits EOL a week before GA, and here are the pg_upgrade --check steps to run against 19 RC1 now.
Iosevka vs JetBrains Mono vs Fira Code, from the source files: 142 vs 119 columns at 14px, plus VS Code settings and a build that fixes the width.
Apple and Google now let you export passkeys, so lock-in is mostly solved. One phone holding all of them isn't. Here's the two-key backup setup.
WebSockets beat SSE by 10% on bytes and 10 KB per connection in Node. Why SSE still fits most apps, plus the nginx and ALB settings that keep it up.
The new lazy keyword cut CLI startup by 72%, the global switch broke pandas, and torch still has no 3.15 wheels.
Sonnet 5.5 cost 14x more per run than Haiku 5.5 and passed no more runs. What fixed it was telling the agent which helper in the repo was dead.
A practical look at sp.h and the recurring attempt to fix C's standard library — what to evaluate, when these libraries help, and when they don't.
Why brute-force counterexample search collapses in large combinatorial spaces, and which techniques (SAT solvers, simulated annealing, learned policies) actually work.
Comparing Gemini 3.5 Flash, Claude Haiku 4.5, and GPT-4o mini with migration code and honest tradeoffs from production use.
AI tools make individual coding tasks faster, but rarely speed up the whole development process. Here's why — and what actually moves the needle.
Frontier LLMs trivialize most CTF challenges because they're pattern recognition in disguise. Here's how to design challenges that actually hold up.
Reading more architecture books won't fix your tangled codebase. The real bug is a broken feedback loop — here's how to repair it with ADRs and regret logs.
Permissive licenses cover code, not brand. Here's why open-source forks get cease-and-desist letters and a practical step-by-step to rename one cleanly.
How to build reliable LLM classification pipelines for high-stakes decisions — fixing confidence calibration, output validation, and human escalation.
Matt Pocock's dictionary-of-ai-coding repo demystifies AI jargon. Here's why shared vocabulary matters and which terms you should actually learn.
Aviation solved the automation skill-decay problem 30 years ago. Here's how their framework applies to AI coding tools and what developers should do about it.
Senior Python interviews often test trivia over real skills. Here's how to handle the gotcha questions and what actually matters for preparation.
AI coding assistants can rebuild the mental context you lost on abandoned side projects. Here's a practical workflow for reviving stalled repos and actually shipping them.
Fix your AI coding assistant's generic outputs by building custom skills — modular instruction sets that give it the domain context it needs.
Why talented engineers write bad code at big companies, explored through the lens of authentication — plus a practical comparison of auth tools.
r/programming banned all LLM content, sparking a major debate about AI fatigue in developer communities. Here's what it means for how we evaluate tools.
AI-generated code creates comprehension debt that slows debugging. Here's a practical process for balancing AI tools with hand-written code.
AI agent orchestration code becomes unmanageable fast. Here's why general-purpose languages struggle with AI workflows and how DSL-based approaches solve it.
A step-by-step guide to safely migrating LLM integrations when new model versions release, with practical code examples for shadow testing and defensive parsing.
When AI generates most of your code, maintenance becomes the real challenge. Here's how to prevent context loss, inconsistency, and silent rot in AI-heavy codebases.
Stop drowning in theory. Build a working compiler with recursive descent parsing and a stack VM — no Dragon Book required.
AI-generated code often breaks in production due to hidden assumptions. Here's how to audit vibe-coded projects and build a workflow that actually holds up.
Stop reading code blind. These git commands give you a mental map of any codebase in five minutes — who works on what, where the bugs are, and which files matter most.
The 1999 satirical essay on unmaintainable code is still painfully accurate. Here's how to identify and fix the most common patterns it describes.
When vibe-coded projects break down, here's how to diagnose the mess, fix state spaghetti, eliminate duplication, and build maintainable code going forward.
A practical guide to migrating from AI-dependent to AI-augmented development, with real auth code examples and tool comparisons.
Feeling like AI tools killed your love of coding? Here's why it happens and a practical framework to bring back the satisfaction of solving problems yourself.
Step-by-step guide to diagnosing and recovering from Git repository corruption, from broken HEAD refs to damaged packfiles.
