Ramsay Research Agent — April 11, 2026
Top 5 Stories Today
1. Karpathy Retires 'Vibe Coding' on Its Birthday, Says the Real Work Is 'Agentic Engineering'
One year ago today, Andrej Karpathy fired off what he called a "shower thought throwaway tweet" and accidentally named an entire industry. Vibe coding. The numbers since then are staggering: 92% of US developers have adopted vibe coding practices, the AI coding market hit $8.5B, and 60% of new code is now AI-generated. Collins English Dictionary named it Word of the Year 2025. Karpathy's retrospective got 8,737 likes. He says he still can't predict tweet engagement after 17 years on Twitter.
But here's the part that actually matters. Karpathy himself proposed "agentic engineering" back in February 2026 as the professional successor. The distinction isn't semantic. Vibe coding means accepting AI output on feel. Agentic engineering means orchestrating agent teams under structured oversight, with context architecture (CLAUDE.md, rules files), recursive arguing between Actor/Evaluator agent pairs, and persistent project conventions.
The timing is loaded. Developer favorability toward AI tools dropped from 77% in 2023 to 60% in 2026, even as adoption soared past 90%. Only a third of developers trust AI-generated code for accuracy. That's the usage-trust gap, and it's the defining tension right now. People use these tools because they're faster, not because they believe in the output.
I see this in my own work every day. A year ago I'd accept Claude's first output more often than not. Now I run structured specs, test gates, and code review loops before anything ships. The output is dramatically better, but the workflow looks nothing like "vibing." It looks like engineering with different tools.
Ars Technica covered the cultural side too. Bluesky users have turned "vibe coding" into a catch-all for every software failure, from broken websites to government IT glitches. The term went from technical description to mainstream blame-shifting in twelve months. That cultural baggage is part of why Karpathy wants to retire it.
A viral tweet about running ruff and vulture to clean dead code from vibe coding sessions hit 6,359 likes and 671K views. The community is moving from "how to vibe code" to "how to maintain vibe code." That's the maturation signal.
What builders should do: stop calling what you do "vibe coding" if you're doing anything serious. Set up CLAUDE.md files, use AGENTS.md for workflow sequencing, run automated quality gates. The tools are there. The era of casually accepting AI output is done.
2. Addy Osmani Open-Sources 19 Google Engineering Practices as AI Agent Slash Commands
Google's Addy Osmani dropped Agent Skills this week, and it hit 2,500+ likes and 340+ retweets before I finished reading the README. Seven slash commands: /spec, /plan, /build, /test, /review, /code-simplify, /ship. Each one encodes quality gates from Google's internal engineering practices, packaged as Markdown skills that work with Claude Code, Cursor, Antigravity, and any agent that accepts Markdown input.
This solves a problem I've been banging my head against. AI agents skip specs. They skip security reviews. They optimize for "done" over "correct." You can tell them not to in your system prompt, but they drift. Osmani's approach is different. Instead of telling the agent what not to do, you give it a structured workflow that forces the right sequence. /spec before /plan. /plan before /build. /test and /review before /ship.
The 19 practices span the full development lifecycle, and they're opinionated. This isn't a generic framework. It's one senior engineer's distilled experience from years at Google, turned into executable agent instructions. The /code-simplify command alone, which focuses on reducing complexity rather than adding features, addresses one of the biggest failure modes I see in AI-generated code: unnecessary abstraction.
What makes this land for me is that it connects directly to Karpathy's agentic engineering thesis. Vibe coding is accepting what the agent gives you. Agentic engineering is directing the agent through quality gates. Osmani's skills are the quality gates, made portable and open source.
I installed the /spec and /review commands into my Claude Code setup yesterday. First impression: the spec command forces a level of upfront thinking that I was skipping, and the output is better for it. The review command catches things I'd normally catch on my third read-through, which saves me the first two.
For builders: grab the repo, install the slash commands that match your workflow, and customize them. The real value isn't the specific commands. It's the pattern of encoding your team's engineering practices as executable agent skills instead of hoping the agent follows written instructions.
3. Anthropic Launches Claude Managed Agents at $0.08/hr. Sentry and Notion Are Already Shipping.
Anthropic released Claude Managed Agents in public beta on April 8 and the pricing stopped me mid-scroll. $0.08 per runtime hour. That's roughly $58/month to run an agent 24/7, plus token costs. For context, I've spent more than that on a single debugging session with Claude Code.
The architecture is what caught my attention though. Anthropic published a separate engineering blog post detailing the Brain/Hands/Session separation. Brain handles reasoning. Hands are stateless containers for code execution, provisioned on demand. Session is an append-only event log that enables wake() recovery after crashes. By starting inference immediately from the session log rather than waiting for container provisioning, they hit a 60% TTFT improvement at p50 and over 90% at p95.
Real companies are already shipping on this. Sentry built a fully autonomous bug-to-PR agent. A bug comes in, the agent triages it, writes the fix, opens the PR. Notion lets teams delegate coding and spreadsheet tasks to Claude without leaving their workspace. Asana's CTO says they shipped advanced features "dramatically faster" than before.
This eliminates months of infrastructure work. I've built agent systems from scratch. The sandboxing alone takes weeks. Permissions, state management, error recovery, container orchestration. All of that is now absorbed into the platform. If you're a team that was planning to build agent infrastructure, the build-vs-buy calculus just flipped hard toward buy.
The timing with Osmani's Agent Skills isn't coincidental. You can now define what an agent should do (Agent Skills), deploy it without managing infrastructure (Managed Agents), and pay by the hour instead of burning engineering time on plumbing. The full agentic engineering stack is assembling itself in real time.
For builders: if you've been running agent workloads on raw API calls with your own state management, evaluate whether Managed Agents can replace your infrastructure layer. The $0.08/hr pricing makes the comparison straightforward. Calculate your current infrastructure cost per agent-hour and compare.
4. GLM-5.1 Becomes the First Open Model to Crack Code Arena's Top 3. It Costs a Third of What Opus Does.
Zhipu's GLM-5.1 ranked third on Code Arena, jumping 90+ points over its predecessor GLM-5 and landing ahead of GPT-5.4 and Gemini 3.1 Pro. Two separate r/LocalLLaMA threads (491 upvotes and 233 upvotes) confirm this isn't just a benchmark curiosity. Practitioners are paying attention.
The hard numbers: GLM-5.1 topped SWE-Bench Pro with 58.4 (GPT-5.4 got 57.7, Opus 4.6 got 57.3). That's the first time an open-source model has led that benchmark globally. It achieves 94.6% of Opus 4.6's coding performance at roughly one-third the cost.
I want to be careful here. Benchmarks aren't production. SWE-Bench Pro measures one slice of coding ability, and Code Arena rankings shift. But the pattern is hard to ignore. A year ago, open models were interesting for local inference and privacy-sensitive workloads. They weren't serious contenders for production coding agents. That's changing.
The second r/LocalLLaMA thread focused specifically on agentic benchmarks, where GLM-5.1 outperforms everything except Opus 4.6. For builders choosing base models for autonomous agent workloads, especially high-volume tasks where per-token cost matters, this shifts the cost-performance frontier meaningfully.
There's a bigger story here too. Apple's head of cloud told reporters that open-source models will address 90% of use cases. The r/LocalLLaMA community is voting on Qwen 3.6 feature priorities (592 upvotes, 260 comments). And meanwhile, DeepSeek has gone quiet (201 upvotes asking "what happened?"). The open-weight competitive map is reshuffling: Gemma 4, Qwen 3.5, and now GLM-5.1 are the ones to watch.
For builders: if you're running agent workloads where you're paying per token at scale, benchmark GLM-5.1 against your current model on your actual tasks. Not on SWE-Bench. On your codebase, your ticket types, your review standards. If it gets within 90% of your current quality at a third the price, the math does itself.
5. A Solo Founder Built a $401M Company with AI Tools and $20K. The One-Person Unicorn Is Real.
Matthew Gallagher launched Medvi, a GLP-1 telehealth startup, from his Los Angeles home in September 2024. Starting capital: $20,000. Employees: zero. Tools: a dozen AI products. Valuation after one year: $401 million, with margins surpassing incumbents in the space.
This is the proof point that connects every other story today. Agentic engineering (story 1) gives you the methodology. Quality tooling like Agent Skills (story 2) gives you the guardrails. Managed infrastructure at $0.08/hr (story 3) gives you the deployment layer. Affordable open models (story 4) give you the cost structure. Put them together and one person with $20K can build a company worth $401M.
Solo-founded startups now represent 36.3% of all new ventures according to Scalable.news, up dramatically from historical norms. At Anthropic's Code with Claude conference, Dario Amodei predicted the first billion-dollar single-employee company would arrive in 2026 with 70-80% confidence. Gallagher's $401M isn't a billion, but it's close enough to make the prediction feel conservative.
I shipped three solo products last year. Rayni, Document Domain Agents, Goldlink. 360K+ lines of production code, just me. None of them are worth $401M, but the workflow is the same. AI handles the volume. Human taste handles the direction. The bottleneck isn't writing code. It's knowing what to build and having the judgment to evaluate whether the output is good enough.
There's a counterpoint worth acknowledging. GLP-1 telehealth is a specific market with specific dynamics. Gallagher didn't build a general SaaS product. He built a telehealth company in a market with massive tailwinds. The AI tools reduced the team size from "need 30 people" to "need 1 person," but the market opportunity had to exist first.
Still. $20K to $401M in a year. Solo. That's not a rounding error. For builders: the constraint on what you can build alone has shifted. The question isn't "can I build this?" anymore. It's "should I, and for whom?"
Section Deep Dives
Security
CPUID supply chain attack served RAT malware through official CPU-Z downloads for six hours. CPUID's download servers were compromised April 9-10, pushing trojanized CPU-Z 2.19 and HWMonitor 1.63 installers containing a Remote Access Trojan that targeted Chrome's IElevation COM interface for credential theft. Kaspersky confirmed it's the same group behind the FileZilla compromise in March 2026. A serial supply chain campaign targeting developer utilities. If you downloaded CPU-Z or HWMonitor in the last 48 hours, verify your hashes immediately.
Vibe coding security costs are now quantifiable: 35 CVEs from AI-generated code in March, up 483% from January. Georgia Tech's Vibe Security Radar tracked the increase from 6 CVEs in January to 35 in March 2026. This is the first academic attempt to measure the security cost of rapid AI-assisted development. Simon Roses Femerling published a field guide in response. The data confirms what I've suspected: vibe coding's productivity gains come with a growing, measurable security debt.
PIArena: first unified platform for prompt injection attack and defense evaluation. Researchers released PIArena, a modular platform that standardizes how prompt injection defenses are tested. The uncomfortable finding: many defenses previously reported as effective fail when tested under diverse attack conditions. If you're running a defense you benchmarked against one attack type, PIArena will show you the gaps.
Multi-agent defense pipeline achieves 100% prompt injection mitigation across all tested scenarios. A recent arXiv paper shows that single-model injection defenses have adaptive attack success rates above 85%. The fix: treat injection defense as a distributed systems problem. Multiple specialized LLM agents in coordinated pipelines detect and neutralize attacks while preserving functionality. This is more expensive but actually works.
Agents
Shopify ships open-source AI Toolkit with native MCP integration for coding agents. Shopify's AI Toolkit, released April 9 under MIT license, gives Claude Code, Codex, Cursor, and Gemini CLI direct access to Shopify docs, API schemas, Liquid validation, and store management via MCP. One-time install, automatic updates. First major e-commerce platform to ship native agent tooling. If you build on Shopify, install this today.
MCP governance goes multi-company: AWS senior principal engineer joins as core maintainer. Clare Liguori (builder of Kiro and Strands Agents SDK) joins the MCP maintainer team to focus on agent execution models. Anthropic's Den Delimarsky promoted to Lead Maintainer. The roster now spans Anthropic, AWS, Microsoft, and OpenAI. MCP is being governed like infrastructure, not a single company's project.
Hermes Agent v0.8.0: 209 merged PRs, MCP OAuth 2.1, and cross-platform messaging. Nous Research's largest release yet adds MCP OAuth 2.1 support, background task notifications, live model switching, consolidated security hardening (SSRF, timing attacks, tar traversal), and plugin system with CLI subcommands. The self-improving agent framework now supports Matrix, Discord, Signal, and Mattermost alongside Telegram and Slack.
Alibaba open-sources OpenSandbox: production agent runtime with gVisor and Firecracker isolation at 9,900+ stars. OpenSandbox provides unified Python/Java/JS/C# SDKs for running coding agents, browser automation, and GUI agents inside isolated containers. Built-in examples for Claude Code and OpenClaw. This fills the critical gap between "I have an agent framework" and "I can run it safely in production."
Okta for AI Agents enters early access ahead of April 30 GA. Shadow AI Agent Discovery automatically detects when employees connect unauthorized AI agents to corporate apps. Universal Directory now treats agents as first-class identities. 88% of organizations report suspected AI agent security incidents, but only 22% treat agents as identity-bearing entities. Agent identity is becoming the new enterprise security perimeter.
Research
ByteRover hits 92.8% on LongMemEval with agent-native hierarchical memory. ByteRover lets the same LLM that reasons about a task curate its own knowledge into a Context Tree. 98.7% knowledge update accuracy, 91.7% temporal reasoning, 96.7% single-session preference. The insight: don't separate memory from reasoning. Let the agent organize its own context.
T² scaling laws rewrite Chinchilla for the inference era. UW-Madison and Stanford researchers show that when you account for test-time compute (sampling multiple answers), optimal pretraining shifts radically into the overtraining regime. Smaller models trained longer become compute-optimal when you plan to use them at inference time. This changes how you should think about model selection for agent workloads.
SAVeR: self-auditing framework prevents belief drift in long-horizon agents (ACL 2026). SAVeR generates persona-based diverse candidate beliefs, then runs adversarial auditing to catch logical constraint violations. Addresses a real problem: coherent LLM reasoning that still violates constraints, with errors propagating across decision steps. Accepted at ACL 2026.
SUPERNOVA extends RL reasoning beyond math and code to general tasks. SUPERNOVA generates verifiable training data for causal inference, temporal understanding, and other reasoning skills where formal verification wasn't previously available. Uses natural language instructions instead of formal verifiers. Significant step toward reasoning that generalizes past the benchmarks it was trained on.
DMax: aggressive parallel decoding for diffusion language models. NUS researchers introduced a new paradigm where diffusion-based LLMs generate multiple tokens simultaneously instead of sequentially. 204 upvotes on r/LocalLLaMA. If the quality gap with autoregressive models closes, this could reshape inference economics.
Infrastructure & Architecture
Cloudflare crosses 500 Tbps network capacity, routes 20%+ of the web. Sixteen years of scaling and a $3B investment in capacity expansion. Cloudflare can absorb the largest DDoS attacks ever recorded. Increasingly positioning as default edge for AI inference workloads alongside CDN and security.
Anthropic Messages API now available on Amazon Bedrock in research preview. Same API shape, but running entirely on AWS-managed infrastructure with zero Anthropic personnel access. Available in us-east-1 with 2M input tokens per minute (expandable to 4M). For enterprises with data-sovereignty requirements, this removes the last objection.
Mem0 memory compression: 90% token reduction with 26% accuracy gain over OpenAI on LOCOMO. Mem0's architecture compresses prompt tokens from ~26K to ~1.8K per conversation while beating OpenAI on accuracy with 91% lower p95 latency. The graph-enhanced variant builds a directed labeled knowledge graph alongside the vector store, with conflict detection preventing contradictory information from corrupting agent memory.
Tools & Developer Experience
Claude Code v2.1.97 ships Focus View and interactive /powerup lessons. Ctrl+O now gives you a distraction-free mode showing only the prompt, one-line tool summaries with edit diffstats, and the final response. The /powerup command launches animated interactive lessons for Claude Code features. Seven versions (2.1.90-2.1.97) shipped this week.
Claudoscope: free open-source macOS app for browsing Claude Code sessions and tracking costs. 100% local, zero telemetry, MIT license. Aggregates token usage across input, output, and cache, maps to Anthropic or Vertex AI pricing, and auto-detects leaked API keys in sessions. If you're running Claude Code daily and don't know what you're spending, this fixes that.
Timescale pg-aiguide: MCP server that teaches AI agents to write better PostgreSQL. pg-aiguide provides version-aware documentation search via semantic and BM25 retrieval, plus curated best practices as machine-targeted guidance. Works with Claude Code, Cursor, Codex, and 40+ other agents. A practical example of domain-specific MCP servers improving code quality in a measurable way.
Claude for Word public beta launches with cross-app integration spanning Word, Excel, and PowerPoint. Anthropic's Microsoft Office integration brings a persistent sidebar to Word for Team and Enterprise users. Edits appear as tracked changes. One conversation thread can span all three Office apps, checking for data inconsistencies across open documents. Explicitly targeting legal: contract review, NDA triage, document-heavy workflows.
Models
Google rolls out Gemini 3.1 Pro globally via the consumer Gemini app. 90.8% on ComplexFuncBench. Completes the 3.1 family rollout across consumer, developer, and enterprise tiers following March's Gemini 3.1 Ultra (2M token context) and Flash-Lite ($0.25/M input). Higher usage limits for AI Pro and Ultra subscribers.
GPT-5.3 Instant Mini quietly replaces GPT-5 Instant Mini as ChatGPT's fallback model. OpenAI upgraded the model users hit when they exceed rate limits. Won't appear in the model picker. More natural conversation, stronger writing, better contextual awareness. The "degraded" experience just got significantly less degraded.
Gemma 4 rapid-fire bugfixes continue: multiple patches in 24 hours. 346 upvotes on r/LocalLLaMA as Google ships another round of fixes. Unsloth updated all Gemma 4 GGUF uploads with corrected chat templates and inference fixes. If you downloaded Gemma 4 GGUFs before April 10, re-download.
Simon Willison: ChatGPT voice mode still runs on a much older, weaker model. Knowledge cutoff is still April 2024, far behind text-based models. If you're building with voice, the capability ceiling is substantially lower than text-mode Claude or GPT-5.
Vibe Coding
60% MatMul performance bug in cuBLAS on RTX 5090 affects all batched FP32 workloads. A researcher demonstrated that cuBLAS dispatches a tiny kernel for every batched FP32 workload from 256x256 to 8192x8192x8, achieving only ~40% FMA pipe utilization. A custom 300-line implementation hits ~68% on Blackwell. Likely affects all RTX SKUs. If you're running local inference with batched operations, check if you're hitting this path.
Voice matching tip: extraction beats rule-stacking. A practitioner found that stacking rules ("be concise," "use short sentences") produces compliant but lifeless output. The winning approach: extract voice patterns from your existing writing and feed them as exemplars. Description by example, not by constraint. This reverses the typical CLAUDE.md approach and it works.
HolyClaude: Docker workstation bundles Claude Code + 7 AI CLIs + headless browser. 1.95K stars for a single container that packages Claude Code alongside other AI CLIs, a web UI, headless browser, and 50+ dev tools. For CI environments or reproducible agent setups without local installation, this is the fastest path.
Hot Projects & OSS
NousResearch/hermes-agent surges +7,671 stars in a single day to 54.9K total. The only open-source agent with a built-in learning loop that creates skills from experience. v0.8.0 shipped April 8 with MCP integration, cron scheduling, and multi-platform messaging gateways. Agents that compound their own capabilities run-over-run.
Scrapling: adaptive AI web scraping with self-healing selectors at 36K stars. D4Vinci/Scrapling auto-scales from single requests to full crawls. The killer feature: AI-powered selector adaptation when page structures change. For anyone running production scraping pipelines, selector breakage is the #1 failure mode. This fixes it.
Cherry Studio: open-source AI desktop client with 300+ assistants and 20+ provider support at 43K stars. CherryHQ/cherry-studio supports OpenAI, Anthropic, Google, Ollama, LM Studio, and more with unified provider switching and autonomous agent capabilities. Unlike browser-based interfaces, it runs locally. The combination of provider breadth and genuine agent support distinguishes it from simpler wrappers.
Vane: open-source AI answering engine (self-hostable Perplexity) at 33.7K stars. ItzCrazyKns/Vane provides retrieval, synthesis, and citation generation as a deployable TypeScript package. For builders wanting AI-powered search without depending on Perplexity's API.
SaaS Disruption
Intercom Fin crosses $100M+ ARR with outcome-based pricing at $0.99/resolution. From $1M to $100M+ ARR, resolving 2 million customer issues per week. Resolution rates climbed from 27% at launch to 67%+. 8,000 companies use it, handling 80%+ of support volume. Backed by a $1M performance guarantee. This is the clearest proof that outcome-based pricing works at scale. HubSpot and Zendesk are copying the model.
Capital bifurcation is real: $242B in Q1 2026 VC went to AI startups while public SaaS hit first-ever S&P 500 discount. SaaStr reports that 80% of all venture capital flowed to AI startups in Q1. AlixPartners projects traditional SaaS revenue will decline 15-35% over three years. Salesforce is down 30% YTD. AI-native companies command 5-6x valuation premiums. This isn't rotation. It's structural repricing.
AI-native SaaS churn splits on price: sub-$50/month products show 23% GRR vs 70% for premium. ChartMogul's analysis of 3,500+ companies reveals the AI churn problem is a pricing problem. Products above $250/month retain like traditional SaaS (70% GRR, 85% NRR). Below $50/month: catastrophic 23% GRR. If you're building AI-native SaaS, price for value, not for adoption.
Replit raises $400M Series D at $9B valuation, targets $1B ARR by end of 2026. Up from $3B in January. Saudi Aramco partnership. The jump from $3B to $9B in three months says the market is pricing "anyone can code" platforms at the same tier as traditional developer tools.
Policy & Governance
Linux kernel publishes official AI coding assistant guidelines. The formal documentation (324 points, 220 comments on HN) establishes rules for Claude, Copilot, Cursor, Codeium, Continue, Windsurf, and Aider. Key rule: AI agents MUST NOT add Signed-off-by tags. Only humans can legally certify the Developer Certificate of Origin. Contributions must include an "Assisted-by: AGENT_NAME:MODEL_VERSION" attribution tag. The approach is pragmatic. Not banned, not blindly embraced, with contributors fully accountable for every line the AI writes.
Anthropic banning under-18 users by reviewing conversations to verify age. 752 upvotes, 287 comments on r/ClaudeAI. A user was permanently banned after Anthropic's team reviewed their chat history. Active detection via conversation analysis is new and raises questions about the tradeoff between safety enforcement and content review practices.
80% of white-collar workers are refusing or bypassing employer AI tools. Fortune/WalkMe surveyed 3,750 people across 14 countries. 54% complete work manually, 33% haven't used AI at all. Trust gap: 9% of workers trust AI for complex decisions vs 61% of executives. A 52-point disconnect. Workers now lose 51 working days annually to technology friction, up 42% from 2025.
44% of Gen Z workers admit sabotaging their company's AI rollout. Fortune surveyed 2,400 knowledge workers. Methods include entering proprietary info into public AI tools, using unapproved tools, and intentionally generating low output to make AI look bad. 30% cite FOBO (fear of becoming obsolete) as their motivation.
Skills of the Day
1. Install Osmani's /spec command before writing any new feature. The agent-skills repo /spec command forces upfront specification before coding begins. Drop it into your Claude Code skills directory and run /spec at the start of every new feature. You'll catch requirement gaps before they become bugs.
2. Use cross-encoder reranking in your RAG pipeline. Most RAG pipelines retrieve by embedding similarity alone, which misses semantic mismatches. Adding a cross-encoder reranking step (using models like ms-marco-MiniLM-L-12-v2) between retrieval and generation typically yields 18-42% precision improvement on domain-specific queries with minimal latency cost.
3. Run ruff check --select F841,F811 and vulture weekly on any AI-generated codebase. Vibe coding leaves dead code. F841 catches unused variables, F811 catches redefined unused functions, and vulture finds unreachable code paths. 6,359 likes on this tip because it hits a real pain point. Automate it in CI.
4. Set CLAUDE_CODE_NO_FLICKER=1 in your shell profile. Enables flicker-free alt-screen rendering with virtualized scrollback. Eliminates visual jarring during long tool output sequences. Small change, big quality-of-life improvement for daily Claude Code users.
5. Feed voice exemplars to Claude instead of stacking style rules. Extract 3-5 paragraphs of your best writing and include them in your CLAUDE.md as examples rather than prescriptive rules. Practitioner testing shows this produces output that actually sounds like you, while rule-stacking produces compliant but lifeless text.
6. Check your RTX GPU's batched FP32 MatMul performance against the cuBLAS bug. The 60% performance regression in cuBLAS dispatches the wrong kernel for all batched FP32 workloads on RTX GPUs. Profile your local inference with nsys or ncu to see if you're hitting the simt_sgemm_128x32_8x5 kernel path. If so, you're leaving 40-60% performance on the table.
7. Use three-tier CLAUDE.md hierarchy for projects over 100 files. Global user preferences at ~/.claude/CLAUDE.md, project-level conventions at ./CLAUDE.md, and directory-local rules at ./src/api/CLAUDE.md. A 650-file case study shows path-based rule scoping prevents context pollution and keeps agent behavior focused on the code you're actually editing.
8. Re-download Gemma 4 GGUFs if you got them before April 10. Unsloth updated all uploads with corrected chat templates, explicit <|think|> thinking control, and fixes for grad accumulation exploding losses (300-400 down to 10-15). Old versions have broken chat behavior and a 26B/31B inference IndexError.
9. Benchmark Gemma 4 31B for document-heavy agentic tasks, Qwen 3.5 27B for code generation. Head-to-head testing on consumer hardware (RTX 3090 Ti, 96GB RAM) shows Gemma 4 jumped from 13.5% to 66.4% on multi-needle retrieval, making its 256K context actually usable. Qwen 3.5 wins on MMLU-Pro, GPQA Diamond, and LiveCodeBench. Match the model to the task type.
10. Add an "Assisted-by" tag to AI-contributed code following the Linux kernel convention. The kernel's new formal policy uses Assisted-by: AGENT_NAME:MODEL_VERSION attribution. Even if you're not contributing to the kernel, adopting this convention in your own repos creates an audit trail for which code was AI-assisted. When the CVE comes (and the Georgia Tech data says it will), you'll know where to look.
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