desgunss retweeted
This prompt turns an AI agent from "writes code however it feels" into "finishes the job on its own"
Drop it into CLAUDE.md and forget the endless "now fix this one more thing":
1. Plan first. Any task with 3+ steps starts with a plan. If something goes sideways, stop and re-plan instead of pushing through
2. Subagents for the side work. Research and parallel analysis go to separate agents, so the main context stays clean
3. Self-improvement loop. Got corrected? Write the rule into tasks/lessons.md. The same mistake never happens twice
4. Not "done" until it's proven. Tests, logs, a diff against main. Ask yourself: "Would a staff engineer approve this?"
5. Elegance, without the fanaticism. Feels hacky? Redo it with everything you know now. Don't overcomplicate small fixes
6. It fixes bugs itself. Got an error, a log, or a failing CI? It sorts it out without hand-holding
Plus three principles: simplicity, root causes instead of patches, and touching as little code as possible
Full text in the video above.
Bookmark this. You'll want it tomorrow, and you won't feel like hunting for it.
desgunss retweeted
Holy f*ck, xAI quietly dropped 56 official Grok bot use cases, so I built a full tier list ranking all of them.
Covering everything from absolute must-haves you need to deploy right now, to niche tools barely anyone will touch.
Scored every single bot on 3 key metrics:
1. how much human babysitting it needs
2. actual hours saved per week
3. real ROI for builders and small teams
56 bots mapped across S, A, B, and C tiers.
Pause and zoom in on the video to check out the details.
Start with the S-tier, then pick the rest based on your niche.
Read my article below and bookmark this tier list so you don't lose it ↓
desgunss retweeted
RAG latency is killing your production pipelines.
Here is how Deep Fusion Memory (DFM) solves it with zero-search parametric injection ↓
Right now, I’ll explain exactly why you should start using Opus 5.5 to make huge amounts of money.
• Specs: 1M-token context window and up to 128K tokens generated per single response.
• Coding & Agents: Scored 66.4% on Terminal-Bench 4.0 (vs. 52.3% for Opus 5); coordinates sub-agents and patches vulnerabilities.
• Style & Safety: Delivers direct, concise responses (key info first) and boasts an 85% reduction in sandbox breakout attempts.
• API Pricing (Standard): Input tokens at $4 (down from $5) and output tokens at $20 (down from $25).
• API Pricing (Features): Cache reads at $0.20 (60% cut); Fast Mode (2.5x speed) at $8 input / $40 output.
• Availability: Accessible via Claude Pro, Max, and Team plans, API, and cloud platforms (AWS, Google Cloud, Azure).
THIS DAMN AI MODEL IS CRUSHING ITS COMPETITORS. I will prove this to you now.
1. 💻 Coding & IT
• Extreme Speed: Migrates 680,000 lines of code in less than a day (a task that would take a human engineering team weeks).
• Refactoring Precision: Successfully fixes performance bugs in 39 out of 40 cases without altering the program's intended behavior (unlike Opus 5).
2. 📈 Business & Financial Analytics
• Flawless Error Detection: Automatically catches logical mistakes within human-provided valuation instructions. No other AI model has ever achieved this.
• Superior Efficiency: Conducts complex corporate merger audits 50% cheaper and 30 minutes faster than Opus 5, committing zero errors.
3. 🔍 Research & Fact-Checking
• Zero Hallucinations: Successfully passed a rigorous data-verification test in 16 out of 18 attempts, checking every number against primary sources. Competitors (Fable 5.1 and Opus 5) failed completely by fabricating data.
4. 🧠 Knowledge Work & Benchmarks
• Beating the Competitor's Flagship: At a medium effort level, Opus 5.5 outperforms GPT-6 Astra operating at its maximum (high effort) configuration.
• 5x More Cost-Effective: Delivers matching or superior quality to GPT-6 Astra at just one-fifth of the cost, using significantly fewer tokens.
• Record Simulation Score: Achieved a record 1,846 Elo points in the GDPval-AA v2.1 benchmark, which simulates real-world tasks across 44 professions.
5. 🛡 Safety & Alignment
• All-Time Best: Scored the highest results in the history of Anthropic's automated behavioral audits. It is far more resilient to prompt injection attacks and unauthorized actions.
6. 💰 Cost & Speed
• 40% Infrastructure Savings: Overall execution costs are reduced by 40%. Token prices dropped by 20% ($4/$20 per million), and context cache reading is slashed by 60% ($0.20 per million).
• 30% Faster Output: Generates text one-third faster than Opus 5 and comes with higher usage limits for Pro/Team subscribers.
7. ✍️ Communication Style
• Human-Like Writing: Communicates naturally ("writes the way a human writes") and instantly brings the most critical insights to the forefront.
INSANE PAPER
Core Essence: Researchers created an AI agent named Serge, which automatically found and fixed 29 real bugs in the popular Hugging Face Transformers AI library over an 80-day period. Human engineers officially accepted and merged its fixes.
How It Works:
1. Finds persistent issues: Identifies tests that have consistently failed on GPUs for a full week.
2. Checks context: Verifies that human programmers are not already working on the same problem.
3. Writes and tests code: Generates a fix (patch) using a Large Language Model (LLM) and runs tests 5 times before and after the change.
4. Hands over to a human: The AI cannot merge code into the project on its own—it simply submits a ready-made draft for human review and approval.
What is the Benefit for the Everyday User?
Even though the AI agent operates "under the hood" and interacts only with programmers, everyday users (developers using the library and consumers of IT products) gain direct benefits:
1. Software runs more stably: Bugs that humans didn't have time to fix due to tedious routines are now resolved automatically and quickly.
2. Updates roll out faster: Programmers are freed from the boring "Groundhog Day" loop (run a test — wait an hour — change one line — wait again). They can spend this time creating new useful features and improving the product.
3. AI technologies become cheaper and more accessible: The Transformers library powers thousands of AI applications worldwide. Fewer internal glitches and lower maintenance costs mean the neural networks, chatbots, and services we use daily become more reliable and affordable.
