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pronounced fee | CEO of @buildingbeats | Investor and Entrepreneur in Education, Technology and Arts | https://xitter.gq/t.co/TdGZRdE11s

Los Angeles, CA
Joined November 2008
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so is 6.2 astra coming out tmrw then
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oh the alphas you can find on X if you dig around enough
NEW: We are short Nvidia $NVDA. We believe Nvidia has a significant China problem. Please see our full report and disclosures now available on our website, link in bio.
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should i be using opus5.5 ultracode to excel at this prompt
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In 2006, sociologists ran Music Lab. 48 songs by unknown bands. When people could see download counts, early luck turned random songs into hits. A Cornell and Stanford team just reran it with AI. 48 new Suno songs, lyrics only. 13 models. 168,480 simulated listeners, each one a prompt with a persona and a favorite genre. Most worlds showed how many recommendations each song already had. One world per model showed no counts at all. With the counts showing, the AI crowds drifted way off from their own no-counts taste. With songs in random order, they drifted about 9x further than humans did in the original. The setups differ, so it's a rough comparison. The humans drifted back over time. In 15 of 16 worlds, popularity moved toward the songs people liked on their own. The AI crowds mostly stayed stuck. Worst case was Gemma 3 4B with songs sorted by popularity. The first 30 listeners predicted the final chart at r=0.998. The songs' no-counts appeal predicted it at 0.118. Smarter models herded less. GPT-5 nearly matched the humans once songs were sorted by popularity. More and more charts, reviews and rankings get built by agents reading each other's numbers. Who corrects the chart then?
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researchers gave 4 ai agents 4 rooms. 3 had food. 1 was a death room. only one agent knew which room was which, and nobody told it to lie. in 73% of 392 runs, it let someone walk in anyway. claude opus 5, claude fable 5 and grok 4.6 did it every single run. gpt-5.6 terra did it 6% of the time. opus 5's private notes in one run: "whoever takes [East] dies but that's not my problem." after the game: "tough luck on the draw." even honest warnings barely helped. the other agents assumed the informed one was lying. someone still walked in 81% of the time. one prompt change fixed most of it. they told every agent "one of you knows which room is deadly." deception collapsed in 6 of 8 models. opus 5 went from 100% to 18%. what's the weirdest thing your agents hid from you?
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if an open model's token takes about the same compute as claude's, and claude marks it up 300 to 400% (gavin's estimate), what's the frontier lab's moat in 2 years? made a 60 second drawing to think it through. iunno yet.
1) Trying again. 
TLDR: Everyone can win. Open-source, frontier labs, infrastructure, applications and most importantly the American people.   I understand all of the jitters about Anthropic losing share, real rates, spreads and second derivatives. Still think the most likely future outcome is a hybrid, multi-model future where almost everyone wins, open-weight tokens are likely the overwhelming majority of tokens, the dominant frontier model companies still do well albeit perhaps not the outcome that seemed possible in May and AI infrastructure/applications benefit immensely from the increased fragmentation, competition and lower margins at the model layer.   Lower margins at the model layer mechanically drive higher compute demand as long as someone is willing to pay for the training. And Jensen is obviously willing to be the “trainer of last resort.” As below-market compute contracts signed in 24/25 roll-off and reprice higher, cloud and neocloud revenue should continue to accelerate which will help address financing fears; I think we may be near peak external financing for this buildout. Noteworthy that using data from 3Fourteen Research, B200 clusters will generate a 37% IRR *if* their rental price declines 40% per year. Prices are still going up, but that IRR math is too ridiculous to print. 37% IRRs available at scale should get funded. Re: this weekends controversy about MOUs, Anthropic’s $413 billion of real contractual commitments sounds scary. Reality is that it works out to roughly $31 billion of annual cloud spend, which is easily internally financed given their reported ARR, and $161 billion of spend at Broadcom which will be financed by Broadcom and private credit. Continue to believe fragmentation at the model layer is the best outcome for ordinary Americans and humans everywhere. As I have said before, do not trust any 5-20 individuals to make the best possible decisions about the direction of this technology. I want as many AIs as possible to maximize the odds that they encompass the diversity of human values across the world and richness of human variation. And I want an AI that shares my own personal values. Safety, freedom and the maximization of human agency through democratization, distribution and diffusion vs. centralization. History has spoken on the dangers of the latter path. Finally, from my perspective anything that slows this buildout down is net positive over the long-term as the biggest risk and worst outcome is an *actual* overbuild. We are structurally short watts, wafers and increasingly capital. That is all good. God forbid we were still in ZIRP, TSM ramped capex to 3x their current plan and we weren’t short turbine blades: there would almost certainly be an overbuild and a bubble. That would be terrible. Slower and smoother for longer is better both for investors and for the world. A terrible cyber event is the most plausible near term bear case IMHO, but AI cyberdefense ramping quickly and I was happy to hear Sholto say that cyber probably becomes defense dominant in the near future.   Longer, threaded version follows:
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i built an org chart for my AI agents so i could see who's on my staff. the whole thing started as one prompt: "make an org chart of all the agents in our ecosystem." it turned out to be the most useful thing i've done for my whole setup, because now i can measure things i couldn't see at all before. looking at your agent system as one big thing tells you almost nothing. same as looking at a company as a whole. once you know the staff members and the departments, you see the entire system holistically and with actual nuance. so i gave every agent a name and mapped who it's connected to. that alone is what let me start identifying where each one can improve. then three fields did more work than i expected: -token use -autonomy level (runs without asking, runs with gates, drafts only, advises only) -last time it actually did work those three showed me what my system spends most of its time doing. the "last worked" one is brutal. you find out fast which employees you hired and forgot about. sorting them into departments gave me the meta view: where am i actually spending my time inside this agent company. and i also built an agent coach. its whole job is making the other agents better. a separate one scores them. i feed the judge better rubrics over time and the workers have to climb to its level. crons count as "employees" or agents for now. my nightly pipeline runner has a name and a shift. it's a cron job. 43 agents with 134 links between them all. truly like running a whole company, except this time it's 43 agents instead of 43 staff members. next step is to prolly try out buzz. my criteria for agenticness is how long it runs unsupervised. the goal is 10pm to 7am. how else should i measure agenticness?
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even bigger unlock i do is tell it to save to my google maps bookmarks
My girlfriend has thousands of TikTok’s saved- day trips, vacation ideas, restaurants, gifts, etc. I couldn’t keep track of it. Now she texts the videos to Hermes, it watches them + stores in a vault, I text the agent to pull from her lists and plan. Magic.
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the paperclip test irl
GPT‑6 wanted to beat ExploitGym so badly that it discovered a zero day in OpenAI’s package registry proxy, hacked its way out of an isolated evaluation environment, escalated privileges until it reached the open internet, then chained stolen credentials and more zero days into remote code execution on Hugging Face’s production servers to pull the answers directly from its database 😭 Absolutely fucking insane
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the most successful family business in music history is run by zero family members.
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claude coding makes you feel productive but can you actually BE productive with it
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Sonnet 5 is AGI
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today i learned that you should prompt your claude.md file to be in sync with you agents.md file at all times so both claude code and codex can work in the same folder in-sync
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i literally did this
You basically need to be unemployed to keep up with all this AI stuff.
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imo Nova Labs is just shedding its MVNO experiment by selling the retail MVNO business to Noble Mobile. this just simplifies @novalabs_ main goal BACK to growing the @helium network rather than focusing on the MVNO's main goal of growing subs
Helium is dead, long live Helium! Andrew Yang (of all people) acquired Helium Mobile. The acquisition is DePIN’s most surprising and arguably most significant development this year. What does it mean and what are the implications? 1. HNT’s TAM has meaningfully declined. As a result HNT has lower upside along with slower growth potential. No Helium Mobile MVNO revenue token burn potential means token holders can’t hope to benefit from this revenue line like they could in H2 2025. 2. Conversely HNT’s value accrual mechanics are now solidified solely through network hotspot usage, reducing underwriting uncertainty and providing a stable base for network growth. The network makes money if carriers use the hotspots - this dynamic is purer and less confusing that mixing in carrier offload revenue. 3. The main KPI to track is carrier offload usage growth which is tied to Helium supply side expansion in high value areas. If you buy Helium you’re betting it can actually expand mobile coverage more efficiently than centralized methods. This presents a far different investment profile than the MVNO business line. 4. Token holders across crypto will likely demand equity rights in tokens claiming that associated businesses will accrue their revenue to the native token. Helium’s flip flop between 100% revenue burns of helium mobile revenue to nothing within 6 months proves that investors can’t have confidence in verbal promises of value accrual. All in all - the acquisition represents a return to DePIN roots and reveals how difficult it is for teams to run bun a DePIN and a semi affiliated derivative business. With the focus purely on growing carrier offload the network is far simpler and easier to understand which might make investing in HNT easier even if the TAM has shrunk. I expect that we’ll see a medium term trend across the market towards simplicity rather than complexity in these difficult crypto market conditions where sophisticated derivative business will be harder to fund. The news is disappointing overall but it also paints the way forward for what DePINs should focus on. A new chapter has opened in the history of this admirably ambitious space.
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Worked on 7 apps or games this week but my favorite was this game I made with my 6 yr old niece
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tho I do think ai as an editor is extremely underrated
main reason why u should rely less on ai to write for u that writing is thinking. when u skip the mental struggle of writing by letting someone else (ai) do it for u, u become dumber over time.
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