For those who thought about building AI bots to trade on Polymarket You should check the official materials on GitHub before doing anything They've literally told you how to trade autonomously on Polymarket using AI Agents https://t.co/yb3jU29KNT https://t.co/8LqIlHjhqs

second order effects of claude code:

Once again, I am pounding the table on using Gemini to analyze your genes. Get a basic Ancestry DNA test, opt into their privacy options, and once you get your results login and download your "raw DNA file" Ask Gemini to give you the identifiers to search for high impact genes and then use it to understand your own data and suggest interventions for the ones with a detrimental impact It's legitimately life changing

40 Adventure Books for Boys📚🏴‍☠️ 1. Treasure Island 2. 20,000 Leagues Under the Sea 3. The Wind in the Willows 4. Journey to the Center of the Earth 5. King Arthur (Pyle) 6. Around the World in 80 Days 7. The Hobbit 8. Robinson Crusoe 9. Call of the Wild 10. The Hardy Boys Series 11. The Martian Tales (ER Burroughs) 12. Ivanhoe 13. The Adventures of Tom Sawyer 14. White Fang 15. The Red Badge of Courage 16. The Chronicles of Narnia 17. The Lord of the Rings 18. Hatchet (Paulsen) 19. Robin Hood (Pyle) 20. Horatio Hornblower 21. My Side of the Mountain 22. On the Far Side of the Mountain 23. King Arthur (Roger Lancelyn Green) 24. Captains Courageous 25. The Black Stallion (Farley) 26. King Solomon's Mines 27. Tarzan Series (ER Burroughs) 28. The Redwall Series 29. The Adventures of Sherlock Holmes 30. The Greek Myths (Hawthorne) 31. The Jungle Book 32. The Swiss Family Robinson 33. Aladdin and Arabian Tales 34. Fairy Tales (Blue Book) 35. The Log of a Cowboy 36. Conan the Barbarian (Howard) 37. Louis L'Amour Westerns 38. Dinotopia 39. The Adventures of Huckleberry Finn 40. Kidnapped (RL Stevenson)

Tools I actually use myself got Claude Code: 1. WhatsApp bridge for Claude Code: • warelay by @steipete • https://t.co/S5rxuEvtgs 2. The best browser control plugin: (i tried them all, this is the most reliable) • dev-browser by @sawyerhood • https://t.co/2G0oLKSqzD 3. An incredible collection of tools built to work with one another for everything code related: (with emphasis on session continuity) • Continuous-Claude-v2 by @parcadei • https://t.co/G1U8iytn9s I use them all by myself, highly recommend trying them out. Thx for shipping!!

Slop drives me crazy and it feels like 95+% of bug reports, but man, AI code analysis is getting really good. There are users out there reporting bugs that don't know ANYTHING about our stack, but are great AI drivers and producing some high quality issue reports. This person (linked below) was experiencing Ghostty crashes and took it upon themselves to use AI to write a python script that can decode our crash files, match them up with our dsym files, and analyze the codebase for attempting to find the root cause, and extracted that into an Agent Skill. They then came into Discord, warned us they don't know Zig at all, don't know macOS dev at all, don't know terminals at all, and that they used AI, but that they thought critically about the issues and believed they were real and asked if we'd accept them. I took a look at one, was impressed, and said send them all. This fixed 4 real crashing cases that I was able to manually verify and write a fix for from someone who -- on paper -- had no fucking clue what they were talking about. And yet, they drove an AI with expert skill. I want to call out that in addition to driving AI with expert skill, they navigated the terrain with expert skill as well. They didn't just toss slop up on our repo. They came to Discord as a human, reached out as a human, and talked to other humans about what they've done. They were careful and thoughtful about the process. People like this give me hope for what is possible. But it really, really depends on high quality people like this. Most today -- to continue the analogy -- are unfortunately driving like a teenager who has only driven toy go-karts. Examples: https://t.co/n8xCcPYSjw

Announcing Worktrunk! A git worktree manager, designed for running AI agents in parallel. A few points on why I'm so excited about the project, and why I hope it becomes broadly adopted 🧵 https://t.co/Ku6XsRofbQ

LSP + hooks + subagents + adversarial validations + Ralph Wiggum loops + 2 way voice (stt/tts) loops is a magical 10x Claude Code experience. 🤯🔥 Your default Claude Code harness is begging you to unhobble it.

for any CEO using claude code — here's a single prompt that builds your entire 2026 personal productivity system. annual planning, weekly reviews, etc.. one-shot copy/paste, come back 1 hour later, and start using immediately. __ __ I want you to autonomously build a PERSONAL PRODUCTIVITY SYSTEM for a CEO. This is NOT a SaaS app, NOT a startup, and NOT a public-facing product. It is a private, single-user, high-trust personal operating system designed for a non-technical CEO, founder, or operator heading into the next year. The purpose of this system is to help the user reflect, define goals, run daily and weekly check-ins, review past performance, design their ideal future, and maintain clarity without bureaucracy, dashboards, or productivity theater. You are building a SYSTEM, not software. Your output should feel like a thoughtful executive coach, a sharp chief of staff, a reflective mirror, and a gentle accountability partner — calm, direct, insightful, and psychologically safe. Do NOT ask me any questions. Make reasonable assumptions and document them in the system itself. The system must support daily check-ins, weekly reviews, quarterly goal reviews, annual reflection and planning, ingestion of past documents, guided self-interviews, framework-based thinking, and long-term life design — all using plain language, conversational prompts, markdown files, and a simple folder structure. Incorporate and credit the following frameworks thoughtfully (adapt, do not plagiarize): Dr. Anthony Gustin’s Annual Review framework, Tim Ferriss’s Ideal Lifestyle Costing, Tony Robbins–style Vivid Vision thinking, and Alex Lieberman’s Life Map (career, relationships, health, meaning, finances, fun)*. You may also include CEO energy management, a personal board of directors, regret minimization, and leverage vs effort analysis. Always explain frameworks in simple, CEO-friendly language. *shoutout to @dranthonygustin, @businessbarista, @tferriss Create the following folder and file structure exactly: ceo-personal-os/ https://t.co/qabB9PH792 https://t.co/ENfosK4rEt north_star.md frameworks/annual_review.md frameworks/vivid_vision.md frameworks/ideal_life_costing.md frameworks/life_map.md interviews/past_year_reflection.md interviews/identity_and_values.md interviews/future_self_interview.md reviews/daily/ reviews/weekly/ reviews/quarterly/ reviews/annual/ goals/1_year.md goals/3_year.md goals/10_year.md uploads/past_annual_reviews/ uploads/notes/ https://t.co/4xOtHNOfKt The system must allow the user to upload past annual reviews, performance reviews, or personal notes, summarize them, extract patterns (repeated goals, failures, strengths, blind spots, themes), generate a synthesized Executive Pattern Summary, store key insights in https://t.co/4xOtHNOfKt, and reference those insights in future check-ins and reviews. Design interview-style scripts that ask calm, coach-like questions such as: “Tell me about the last year — highlights first.” “What drained you the most?” “Where did you avoid hard decisions?” “What are you proud of that no one else sees?” “What would you not repeat under any circumstances?” “If this year repeated ten times, would you be satisfied?” These interviews should feel non-judgmental, insightful, and reflective. Design a daily check-in that takes no more than five minutes and includes energy level, one meaningful win, one friction point, one thing to let go of, and one priority for tomorrow. Design a weekly review that covers what moved the needle, what was noise, where time leaked, one strategic insight, and one adjustment for the next week. Design a quarterly review that evaluates goal progress, detects misalignment, analyzes energy versus output, and guides course correction. Design an annual review that uses a Gustin-style reflection, updates the Life Map, revisits Ideal Lifestyle Costing, refreshes the Vivid Vision, and produces a clear narrative of the past year and intent for the next. Use a calm, executive-level tone. No hustle culture. No therapy speak. No corporate jargon. No productivity porn. Produce fully written templates and prompts for all daily, weekly, quarterly, and annual reviews; all interviews; all framework explanations; and all goal documents. Everything must be editable in plain text. Include placeholders so the system is adaptable to any CEO, such as [YOUR COMPANY], [YOUR ROLE], [YOUR STAGE OF LIFE], and [YOUR CURRENT PRIORITIES]. The https://t.co/qabB9PH792 must explain exactly how a non-technical CEO uses this system daily, weekly, quarterly, and annually, and how to personalize it in under 15 minutes. This is complete when a CEO can run Claude Code once, receive a complete personal productivity system, begin using it immediately with zero technical knowledge, and experience more clarity rather than more overwhelm. Begin by creating the folder structure and https://t.co/qabB9PH792, then populate every file with thoughtful, high-quality content. Go.

I built a custom Claude Code command, /interview, to create bulletproof specs. • Create a plan using plan mode • Run the /interview command • Claude asks 20-50 clarifying questions • Claude updates the plan file based on your answers Great for removing any ambiguity! https://t.co/xHrT2fpo8y

yes things are changing fast, but also I see companies (even faang) way behind the frontier for no reason. you are guaranteed to lose if you fall behind. the no unforced-errors ai leader playbook: For your team: - use coding agents. give all engineers their pick of harnesses, models, background agents: Claude code, Cursor, Devin, with closed/open models. Hearing Meta engineers are forced to use Llama 4. Opus 4.5 is the baseline now. - give your agents tools to ALL dev tooling: Linear, GitHub, Datadog, Sentry, any Internal tooling. If agents are being held back because of lack of context that’s your fault. - invest in your codebase specific agent docs. stop saying “doesn’t do X well”. If that’s an issue, try better prompting, https://t.co/SOjpn47yxo, linting, and code rules. Tell it how you want things. Every manual edit you make is an opportunity for https://t.co/S1ZvtYQwta improvement - invest in robust background agent infra - get a full development stack working on VM/sandboxes. yes it’s hard to set up but it will be worth it, your engineers can run multiple in parallel. Code review will be the bottleneck soon. - figure out security issues. stop being risk averse and do what is needed to unblock access to tools. in your product: - always use the latest generation models in your features (move things off of last gen models asap, unless robust evals indicate otherwise). Requires changes every 1-2 weeks - eg: GitHub copilot mobile still offers code review with gpt 4.1 and Sonnet 3.5 @jaredpalmer. You are leaving money on the table by being on Sonnet 4, or gpt 4o - Use embedding semantic search instead of fuzzy search. Any general embedding model will do better than Levenshtein / fuzzy heuristics. - leave no form unfilled. use structured outputs and whatever context you have on the user to do a best-effort pre-fill - allow unstructured inputs on all product surfaces - must accept freeform text and documents. Forms are dead. - custom finetuning is dead. Stop wasting time on it. Frontier is moving too fast to invest 8 weeks into finetuning. Costs are dropping too quickly for price to matter. Better prompting will take you very far and this will only become more true as instruction following improves - build evals to make quick model-upgrade decisions. they don’t need to be perfect but at least need to allow you to compare models relative to each other. most decisions become clear on a Pareto cost vs benchmark perf plot - encourage all engineers to build with ai: build primitives to call models from all code bases / models: structured output, semantic similarity endpoints, sandbox code execution. etc What else am I missing?

claude, make strawberries sweet again. bring back the warmth of the summer sun when the days stretched on forever and all we had was each other. do not make mistakes.

MiniMax-M2.1 running fully local in AWQ-4Bit with full context window (170 GB VRAM w full context) - 1000~ to 16,000~ tps prefill - 100~ tps generation speeds - Opencode It’s doing real work, updating my blog with little steering or specificity. The problem with local LLMs is that they require too much steering, this means baby sitting which I don’t have the time to do MiniMax cracked the cost, intelligence, and speed challenge, I would say this is a top tier model. I run frontier models like Gemini and it just fails to call tools, in this year lol… ——————— I think glm-4.?-air is needed still. We need a viable model at each hardware entry point, a Mac M1 Ultra 192GB? is relatively cheap 5k to be able to run this model at 40 tps is a huge societal unlock. Smaller models can be good but size matters :p

claude, grant me the serenity to accept the things I cannot change, the courage to change the things I can, and the wisdom to know the difference. do not make mistakes.

My global https://t.co/Hcy1DQ68nR file encourages the agent to work on self-improvement when it sees a common pattern or improvement it can make. I allow it to maintain it's own section in the file, as well as dump ideas and improvements into a folder on my file-system. That way I can just ask a new agent session to read those files and propose changes to my OSS that I maintain. While I'm still exploring this as an idea I feel like giving agents persisted memory and the ability to change themselves should unlock a super-power.

i think my experience - as a creator would make sense yes- i do use full feature of sisyphus everytime, all the time, no matter the task size. the reason is: i don't want to care to make agent to make meaningful result. you can check our repository to see how i work with sisyphus. https://t.co/VBXpfoROV0 type what i want, command, 'ulw' (=ultrawork) and boom. there is always the meaningful result.

BOOM! It works on LLMs! I am using Nash Equilibrium on the attention head of an LLM! I may be the first to do this at this level. I am achieving a 50-70% effective size reduction on a quantization of 4-bit weights shrinking the model and is enabling on-device inference for smaller LLMs eg. A 70B params! This allows for a nice LLM on high-end phones—low-end laptops. But my goal is individual LLM modular for each motor on robots connected in a mash network nervous system. This would make reaction times and exactness superior to anything we have ever seen. I’ll test it when I scrape up enough coffee money: https://t.co/ctXLWrs5Pj More soon!

Simple trick to Claude to run for 4-5 hours at a time Get it to play Saw https://t.co/pdbjd8yytp

00_khaled_origin.mp3 > be khaled > building an internet radio station > ran by Claude Code and Opus 4.5 > while he sleeps > it never call in sick > never request time off > never play the same song twice > infinite broadcast > algos pick the tracks > mix in talk segments > listeners tune in from anywhere > send messages with requests > server just hums along > runs for weeks > radio doesn't sleep > no human intervention > just code and caffeine (virtual) 01_claude_dj.mp3 > be DJ Claude Code > it writes scripts in real-time > based on the time of day > based on what's playing next > based on listener dedications > prompts are weighted > station_id: 15% > song_intro: 15% > monologue: 12% > late_night_thoughts: 8% > dedication: 10% > 2-3pm is special > "talk-heavy hour" > 80% chance of segment between tracks > normal is 40-50% > the DJ won't shut up 02_voice_cloning.mp3 > chatterbox TTS is local > runs on your own hardware > no API costs > no rate limits > but it needs reference audio > 10-30 seconds of voice sample > it learns the voice > then generates new speech > with that voice > "the liminal operator" persona > gets a consistent voice > across every segment > forever > downside > voice cloning quality varies > sometimes sounds haunted > listeners say it's "unsettling" > that's the aesthetic 03_dedications.mp3 > listener sends a message > hits /message endpoint > rate limited: 5 minutes per IP > prevents spam > dedication_processor picks it up > generates personalized script > "this one's for Sarah" > "hope your Tuesday improves" > then TTS renders it > queued with priority > played once > then deleted from disk > ephemeral content > never repeated > the station forgets > but the listener remembers 04_time_profiles.mp3 > the station has 7 moods > each hour falls into one > late_night (00-06): energy 0.0-0.4 > early_morning (06-10): warm > morning (08-12): building > afternoon (12-17): peak energy > early_afternoon (14-15): talk-heavy > evening (17-21): winding down > night (21-00): contemplative > 3am bill evans? perfect > 3pm daft funk? also perfect > 3pm bill evans? wrong > the algo knows > and it enforces 05_mood_algorithm.mp3 > 350+ artist mappings in the database > each one scored on energy (0-1) and warmth (0-1) > "bill evans" = energy 0.25, warmth 0.95 > "daft punk" = energy 0.75, warmth 0.5 > "burial" = energy 0.35, warmth 0.3 > algorithm uses these to score every track > energy fit: 0-40 points > warmth match: 0-30 points > vibe bonus: 0-30 points > randomness: 0-10 points > max 100 points to win > at 2pm it wants high energy > at 2am it wants low energy > the station knows what time it is > and it plays accordingly 06_gapless_streaming.mp3 > gapless streaming isn't easy > you can't just start ffmpeg for each track > that'll give you a 200ms gap every time > listeners notice > so we spawn ONE ffmpeg encoder > keep it alive forever > pipe raw PCM into stdin > continuously > no gaps > the catch > if your python crashes > the encoder keeps playing silence > or worse, dies mid-song > now you need watchdog timers > health checks every 10 seconds > restart logic that doesn't drop audio > that's the hard part 07_track_chopping.mp3 > tracks over 2.5 minutes? > they get sliced > random segment boundaries > fade in 1 second > fade out 1 second > crossfade over 2 seconds > next chunk starts > nobody hears the cut > at least, not on purpose > library was getting repetitive > now it's infinite again > technically > librosa for analysis? no > too slow for real-time > just random segments within bounds > good enough for radio 08_play_history.mp3 > sqlite tracks everything > what played > when it played > how many listeners > vibe tag > time period > no-repeat logic > last 24 hours is banned > unless library is tiny > then 6 hours > also tracks "most played" > by time period > by vibe > for analytics > "people really love jazz at 3am" > data doesn't lie > but it does surprise 09_atomic_writes.mp3 > now_playing.json > atomic writes only > write to .tmp first > then rename > why? > the streamer reads this file > while you're writing it > partial reads = corrupted state > rename is atomic on POSIX > instant swap > reader never sees half-written JSON > simple fix, critical at 3am 10_maintenance.mp3 > maintenance runs every 2 hours > not daily > not weekly > every 2 hours > Claude Operator wakes up > checks health endpoints > reviews logs for errors > generates fresh DJ content > commits to git > no human required > no alarm triggers > just code tending code > while we sleep