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Sep 30 – Oct 14, 2026 · Daily
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Showing 401–425 of 1145 mentions
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• twitter 6d negativehormozi explains how winning outbound is about ticket size. "you want to be hunting whales, not hunting minnows, with a harpoon" which makes perfect sense. if you have two campaigns, with the exact same metrics. but one is selling an offer 4x the value. that campaign has 4x the output. it's just a game of math, w… hormozi explains how winning outbound is about ticket size. "you want to be hunting whales, not hunting minnows, with a harpoon" which makes perfect sense. if you have two campaigns, with the exact same metrics. but one is selling an offer 4x the value. that campaign has 4x the output. it's just a game of math, what are you putting in and what comes out. in this article I broke down how we run a full GTM motion using claude opus 5.5 (and how you can setup the same system yourself)view source ↗
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• twitter 6d negativeI'm only going to say this TWICE. If you're under 45, the incoming market CRASH will be a generational time to BUY insanely undervalued stocks at a discount. Here are 5 stocks I believe can 10x: 1) $NVDA https://t.co/o2FDnXExWF I'm only going to say this TWICE. If you're under 45, the incoming market CRASH will be a generational time to BUY insanely undervalued stocks at a discount. Here are 5 stocks I believe can 10x: 1) $NVDA https://t.co/o2FDnXExWFview source ↗
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• twitter 6d negativeYou must be bored mind you Olandria got A list celebrities sticking up for her every new hate train yall try to formulate. Ola’s being treated as peers w these ppl while attending the same events and chelley on her eleventh Beyonce influencer event struggling to get a pic w her https://t.co/PbxIcw7lKz You must be bored mind you Olandria got A list celebrities sticking up for her every new hate train yall try to formulate. Ola’s being treated as peers w these ppl while attending the same events and chelley on her eleventh Beyonce influencer event struggling to get a pic w her https://t.co/PbxIcw7lKzview source ↗
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• twitter 6d negativeI don't recommend anyone scramble to move their funds to new wallets today. But we should take the risks to cryptography from AI-accelerated math seriously, and minimize our exposure to not just quantum-vulnerable cryptography, but also potentially AI-vulnerable cryptography. The core new area of risk from this viewpo… I don't recommend anyone scramble to move their funds to new wallets today. But we should take the risks to cryptography from AI-accelerated math seriously, and minimize our exposure to not just quantum-vulnerable cryptography, but also potentially AI-vulnerable cryptography. The core new area of risk from this viewpoint is, unfortunately, ML-DSA / FHE / lattices. (and it's also another reason, along with quantum, why ECDSA might fall even faster than expected, hence the "fresh address" recommendation) So far most people have been in the mode of thinking "elliptic curves broken, hashes safe, lattices safe". But there is a good chance that the concrete security of lattices will take serious hits from the next two years of AI math. The basic threat model is: factoring is something that naively takes 2^(n/2) time, but over decades smart people have found and optimized number field sieves, and degraded that to 2^O(n^(1/3)), which is why RSA keys and signatures need to be ~400 bytes (and not 64 bytes). What if there are skeletons in the closet like that, both for elliptic curves and lattices, that we are simply not smart enough to discover - but bots soon will be? This is a major part of the reason why for the past year ethereum's lean roadmap has been going in the "hash-only" direction: no lattices, no ML-DSA, no Falcon, no lattice-based commitments inside ZK proofs, etc. Signatures in lean ethereum are all hash-based, either WOTS or SPHINCS-. For signatures and proofs, we already know how to go hash-only. The bigger challenge is for *public-key encryption* - and this goes far beyond blockchains. Secure communication, anonymizing protocols, lots of things need public-key encryption. And unfortunately there are long-standing mathematical theorems showing why public-key encryption cannot be done with hashes alone. You have to have some kind of trapdoor object that has at least one form of usable "structure" - either group theory (incl. isogenies) or lattices or codview source ↗
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• twitter 6d negativeFreedom is: • Nature • Saying no • Road trips • Great hires • Deep sleep • No meetings • 4 hour work days • Do not disturb mode • Controlling your schedule • Spontaneous adventures Entrepreneurship is about freedom. https://t.co/fjU6Iene3s Freedom is: • Nature • Saying no • Road trips • Great hires • Deep sleep • No meetings • 4 hour work days • Do not disturb mode • Controlling your schedule • Spontaneous adventures Entrepreneurship is about freedom. https://t.co/fjU6Iene3sview source ↗
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• twitter 6d negativewhat do y'all use to generate videos? higgsfield is a scam what do y'all use to generate videos? higgsfield is a scamview source ↗
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• twitter 6d negativeholy sh*t. Official Anthropic tip for Claude Code: stop burning Opus 5.5 context on tasks Haiku 5.5 can swarm, while Sonnet 5.5 builds put the architect on call with /advisor claude --advisor opus --subagents haiku Sonnet 5.5 drives the primary session on high effort, writing diffs and running test suites Haiku 5.5 … holy sh*t. Official Anthropic tip for Claude Code: stop burning Opus 5.5 context on tasks Haiku 5.5 can swarm, while Sonnet 5.5 builds put the architect on call with /advisor claude --advisor opus --subagents haiku Sonnet 5.5 drives the primary session on high effort, writing diffs and running test suites Haiku 5.5 subagents swarm the repo in parallel at 340 tok/s, handling file discovery and spec docs lookup Opus 5.5 stays on call in the background as the advisor, only stepping in at three critical moments: → before a plan locks: does the implementation plan miss auth invariants or schema contracts? → when a test breaks twice: are we fixing the root cause or falling into a recursive rabbit hole? → before calling done: did the full diff introduce hidden regressions or break pre-flight rules? Opus 5.5 reviews. Sonnet 5.5 builds. Haiku 5.5 swarms Jev engineering runs the identical pattern one layer down: mechanical decisions that need no reasoning (which file to open, which tool to invoke, retry or abort) execute in 16ms, so the frontier models only wake up when execution paths actually diverge Plan on high. Delegate on medium. Keep Opus on call. - the full advisor setup > Opus 5.5 on call reads full session history and catches deep architectural traps > Sonnet 5.5 lead drives edits, writes core logic, and executes test harnesses > Haiku 5.5 subagents swarm AST parsing, grep scans, and API docs in parallel > JEV micro-fork layer resolves 1,500+ mechanical routing branches in under 16ms > advisor stays completely silent on routine bash execution to protect context paste the setup and this prompt into Claude Code below: "Configure my Claude Code workspace for hierarchical advisor orchestration: 1. Audit ~/.claude/settings.json and project config for model roles fitting lead, subagents, and advisor: > Pin main session model: sonnet with effortLevel: high for primary execution > Pin subagents model: haiku with effortLevel: medium for parallel file discovery andview source ↗
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• twitter 6d negative5 issues have been filed on tsc-rs so far. Of the 5, 4 of them are issues with the official upstream typescript version we ported. This port is so faithful that the vast majority of "issues" are just accurate recreations of real typescript behavior. Wild. 5 issues have been filed on tsc-rs so far. Of the 5, 4 of them are issues with the official upstream typescript version we ported. This port is so faithful that the vast majority of "issues" are just accurate recreations of real typescript behavior. Wild.view source ↗
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• twitter 6d negative3 years ago? There were no reasoning models. Tool calls didn't exist yet. It makes no sense to do this comparison at that time scale, the "tasks" models could do were crappy auto complete. o1 came out in December of 2024. $15/m in and $60/m out. Cached reads were $7.50 (insanely expensive). I genuinely struggled to f… 3 years ago? There were no reasoning models. Tool calls didn't exist yet. It makes no sense to do this comparison at that time scale, the "tasks" models could do were crappy auto complete. o1 came out in December of 2024. $15/m in and $60/m out. Cached reads were $7.50 (insanely expensive). I genuinely struggled to find "real world tasks" that o1 could complete. I chose to make it name skateboard tricks. Cost $0.109 per question. Gemini 3 Flash came out December 2025. It got a higher score, and cost $0.0020. 55x cheaper in a year. Idk why you're fighting on this man, it's so obviously true. Try using o1 for any real code work, then switch to GLM-5.3-flash, and tell me honestly which you'd prefer to use. It's going to be the one that is 10x+ cheaper.view source ↗
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• twitter 6d negativeI got some issues with this. The $132m/year is not real at all. Closer to $3m at today's prices, and as low as $1.2k within a year. Yes, $132m -> $1.2k. Hear me out. 1. The price listed is not accurate at all. $11m/month is just wrong. Real "api cost" is prolly $100k-$500k per month range. The 1.5T tokens number i… I got some issues with this. The $132m/year is not real at all. Closer to $3m at today's prices, and as low as $1.2k within a year. Yes, $132m -> $1.2k. Hear me out. 1. The price listed is not accurate at all. $11m/month is just wrong. Real "api cost" is prolly $100k-$500k per month range. The 1.5T tokens number includes cache'd tokens. I looked at my own usage, and 33.5B tokens came out to $0.56/m (under 1/10th of Peter's $8/m estimate). I'm primarily using expensive models like Opus and Astra, and "1.5t tokens" would still have cost me ~$800k. If I used cheaper models and hit cache more, easily could get as low as $100k. 2. Intelligence is getting cheaper incredibly quickly I know our LLM bills are going up and everyone is stressed about it. Totally reasonable! It's not because intelligence got more expensive. Artificial Analysis tracks how "cost at a given intelligence level" drops over time. It used to drop at ~10x/year. Now it's at 30x lower in 5 months. (see image) Assuming the $100k from before, cost could get as low as $3k/month in a few months, and if trends continue, $100/month by this time next year. Obviously there will be smarter models and we will move to them, but replicating her experience today with equivalent models will be $1.2k/year by next year. 3. Nobody said this is normal Lauren is the 0.0001% of "agentic engineer". Measuring her work as "$ in -> software out" is reductive and wrong. Her spend should be looked at as a combination of product/eng, research, FDE work (pstack), and marketing. Worth $350k/day? No. Worth a mil or two a month? Absolutely. She is exploring what happens when you ignore the cost of intelligence. She's not doing this because Cursor expects every company to spend $10m/year on tokens. She's doing it because that $10m in tokens is going to cost $10,000 in a year or two.view source ↗
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• twitter 6d negativeI really hope everyone copies the work we did here. I’ve been thinking about how many tokens are wasted with bad PR monitoring flows and it made me feel a bit sick I really hope everyone copies the work we did here. I’ve been thinking about how many tokens are wasted with bad PR monitoring flows and it made me feel a bit sickview source ↗
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• twitter 6d negativeIf you’re using an AGENTS.md or CLAUDE.md file that’s more than a month old, it’s likely trying to solve problems that no longer exist and burning tokens on redundant context. Ask your agents to review your environment then suggest optimizations and improvements. If you’re using an AGENTS.md or CLAUDE.md file that’s more than a month old, it’s likely trying to solve problems that no longer exist and burning tokens on redundant context. Ask your agents to review your environment then suggest optimizations and improvements.view source ↗
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• twitter 6d negativeOpenAI's streak of aura losses continue. Lock in! 😭 OpenAI's streak of aura losses continue. Lock in! 😭view source ↗
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• twitter 6d negative"I could have done very bad things to Hillary Clinton... Joe Biden... Barack Hussein Obama. I thought it was inappropriate to do so because you have to treat the office of the presidency with respect." President Trump says he held back from taking “very bad” actions against Hillary Clinton, Joe Biden and Barack Obama … "I could have done very bad things to Hillary Clinton... Joe Biden... Barack Hussein Obama. I thought it was inappropriate to do so because you have to treat the office of the presidency with respect." President Trump says he held back from taking “very bad” actions against Hillary Clinton, Joe Biden and Barack Obama because he believed the presidency demanded restraint. Trump then points to his two impeachments, saying they changed his view of what he could do to political opponents.view source ↗
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• twitter 1w negativeHow did one course generate $354K? 👀 Not luck. Not magic. A strategy. A funnel. And the right execution. I broke down the exact process step-by-step. N How did one course generate $354K? 👀 Not luck. Not magic. A strategy. A funnel. And the right execution. I broke down the exact process step-by-step. Nview source ↗
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• twitter 1w negativeHad an interesting convo with YC F26 founder @an_engineer_log on what he's seen work and fail across real agent products. He's also giving listeners free @Agent37HQ credits. Reply to the original thread if you want to try running your own agent in a sandbox. Had an interesting convo with YC F26 founder @an_engineer_log on what he's seen work and fail across real agent products. He's also giving listeners free @Agent37HQ credits. Reply to the original thread if you want to try running your own agent in a sandbox.view source ↗
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• twitter 1w negativeThe interesting shift is not better extraction. It is moving the boundary between probabilistic and deterministic work. Let the model find the leaves and define the calculation. Then let code own the arithmetic and validation. That makes derived data auditable, reproducible, and much easier to debug. The interesting shift is not better extraction. It is moving the boundary between probabilistic and deterministic work. Let the model find the leaves and define the calculation. Then let code own the arithmetic and validation. That makes derived data auditable, reproducible, and much easier to debug.view source ↗
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• twitter 1w negativeAI coding agents can get stuck in loops where each attempt creates more bugs and more confusing context. In this article, Amir explains why these fix loops happen and how to get out of them. You’ll learn how to write more precise fix requests, use failing tests to guide the agent, and more. https://t.co/zMDsCeQh77 AI coding agents can get stuck in loops where each attempt creates more bugs and more confusing context. In this article, Amir explains why these fix loops happen and how to get out of them. You’ll learn how to write more precise fix requests, use failing tests to guide the agent, and more. https://t.co/zMDsCeQh77view source ↗
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• twitter 1w negativeEven compute poors like me can contribute to autonomous math & science discovery. Before OpenAI’s release, I had exact results including a hazard-mixture counterexample to a stated 2026 theorem, a sharp dimension-dependent softmax threshold with counterexamples above it, stochastic-survival grouping theorems, and weig… Even compute poors like me can contribute to autonomous math & science discovery. Before OpenAI’s release, I had exact results including a hazard-mixture counterexample to a stated 2026 theorem, a sharp dimension-dependent softmax threshold with counterexamples above it, stochastic-survival grouping theorems, and weighted-tree optimality/regret results. Also produced certified lower-bound constructions for no-five-on-a-sphere instances through n=32view source ↗
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• twitter 1w negativeThis must be a bug?? X Premium+ annual "discount" in Denmark: Monthly: 285 kr × 12 = 3,420 kr Annual: 3,378 kr You save 42 kr (~$6). That's 1.2%. Commit to a full year to save the price of a coffee. https://t.co/tRtUTSkHxH This must be a bug?? X Premium+ annual "discount" in Denmark: Monthly: 285 kr × 12 = 3,420 kr Annual: 3,378 kr You save 42 kr (~$6). That's 1.2%. Commit to a full year to save the price of a coffee. https://t.co/tRtUTSkHxHview source ↗
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• twitter 1w negativeLearn accessible forms in this order: 🧭 1. Plan the fields users need 🧭 2. Choose the right input type 🧭 3. Give every input an id 🧭 4. Connect a visible label 🧭 5. Group related controls with fieldset 🧭 6. Add clear instructions 🧭 7. Validate required values 🧭 8. Show errors beside fields 🧭 9. Keep entered values afte… Learn accessible forms in this order: 🧭 1. Plan the fields users need 🧭 2. Choose the right input type 🧭 3. Give every input an id 🧭 4. Connect a visible label 🧭 5. Group related controls with fieldset 🧭 6. Add clear instructions 🧭 7. Validate required values 🧭 8. Show errors beside fields 🧭 9. Keep entered values after an error 🧭 10. Test with keyboard only 🧭 11. Check the full submission flowview source ↗
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• twitter 1w negativeWhy are people saying this freestyle is bad? Why are people saying this freestyle is bad?view source ↗
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• twitter 1w negativeHi @thsottiaux @ajambrosino @OpenAIDevs: please turn on ultrafast for 6.1-sol for $500 plan users. If you do this, I’ll be satisfied for a very long time. If you don’t…😤 Hi @thsottiaux @ajambrosino @OpenAIDevs: please turn on ultrafast for 6.1-sol for $500 plan users. If you do this, I’ll be satisfied for a very long time. If you don’t…😤view source ↗
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• twitter 1w negativeHey @grok, what is this cut even for 😭 https://t.co/jMP5VWyrRx Hey @grok, what is this cut even for 😭 https://t.co/jMP5VWyrRxview source ↗
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• twitter 1w negativeGPT-6.1 Sol [xhigh] on KernelBench-CUDA: Four hand-written CUDA kernels passed their final checks on RTX PRO 6000. Native Sparse Attention is the jump: 0.28 ms, 9x faster than GPT-6 Sol and 5th of 14. The other three land mid-board. Where GPT-6 Sol leaned on cuBLAS and CUDA graphs, 6.1 Sol writes its own tensor-core … GPT-6.1 Sol [xhigh] on KernelBench-CUDA: Four hand-written CUDA kernels passed their final checks on RTX PRO 6000. Native Sparse Attention is the jump: 0.28 ms, 9x faster than GPT-6 Sol and 5th of 14. The other three land mid-board. Where GPT-6 Sol leaned on cuBLAS and CUDA graphs, 6.1 Sol writes its own tensor-core GEMMs and puts whole problems into one cooperative launch. It also stops early: no problem took it more than 80 minutes. One problem per reply below.view source ↗