Hamza

725 posts

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Hamza

Hamza

@HAlfadel

working on my ideas - prev cred, meta, amazon, skyscanner

London Katılım Ocak 2012
798 Takip Edilen159 Takipçiler
Hamza
Hamza@HAlfadel·
@swyx hahah love this
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Alex Macdonald
Alex Macdonald@alexfmac·
I hope this photo is a watershed moment. Forced to carry on to the end of the race with a broken back. Collapsed & died after the finish line. RIP Gold Dancer. If you’re making money off this kind of suffering - how do you look at yourself in the mirror?
Alex Macdonald tweet media
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Thomas Ricouard
Thomas Ricouard@Dimillian·
The build iOS apps plugin for Codex has been updated! It now auto install and setup XcodeBuildMCP with everything you need for tests and UI agentic automation, and we added new App Intents skill to help you expose your features to iOS! Try it in the Codex app!
Thomas Ricouard tweet media
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Hamza
Hamza@HAlfadel·
@kunchenguid look forward to following along and see what you build! 😀
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Kun Chen
Kun Chen@kunchenguid·
Hi, I'm Kun. I just left my big tech job as L8 engineer at Meta, Microsoft, and Atlassian, to be a solo builder. I worked on Bing and MSN, was a lead developer on Facebook Games, and Atlassian's SWE agent Rovo Dev. Doing AMA here for sharing and meeting ppl - ask me anything!
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Bruce Cao
Bruce Cao@bruce_CQT·
hike your todos🗻
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Hamza
Hamza@HAlfadel·
@0xSero would love to try! 👍
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0xSero
0xSero@0xSero·
Do you want to try Droid? I’m doing a giveaway 3 people will win 100M Factory credits each.Thats 5 months of their 20$ a month subscription. Winners selected randomly from comments in 48 hours.
0xSero tweet media
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Nathan Flurry 🔩
Nathan Flurry 🔩@NathanFlurry·
Yep. We built a new operating system. → Powered by WASM & V8 isolates → node, python, bash, git, grep, curl (+129 more) → Mount anything: S3, Google Drive, SQLite, … → Embedded as a library, no special infra → Built on Actors & Secure Exec & Sandbox Agent Thread 🧵
Rivet@rivet_dev

Say hello to agentOS (beta) A portable open-source OS built just for agents. Powered by WASM & V8 isolates. 🔗 Embedded in your backend ⚡ ~6ms coldstarts, 32x cheaper than sbxs 📁 Mount anything as a file system (S3, SQLite, …) 🥧 Use Pi, Claude Code/Codex/Amp/OpenCode soon

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Hamza retweetledi
Massimo
Massimo@Rainmaker1973·
The stunning beauty of an Altay sunset, Kazakhstan
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Patrick OShaughnessy
Patrick OShaughnessy@patrick_oshag·
William on how an early stage employee takes way more risk than a founder: "If I'm making $400-500K at Google or Meta and go to an early stage company to get 1% of this company and make $90,000. I've now changed the trajectory of my life, that's a lot of risk. But as a founder, you're not. It's a much higher likelihood that of the next round, regardless of your company, you'll be able to sell some secondary. If it shuts down, you can get employed at a great company, and you have a CEO on your resume. That first employee, they have first employee at a failed company. That's actually not a great resume line item. So we've de-risked the founder, but we haven't de-risked the early stage employee."
Patrick OShaughnessy@patrick_oshag

.@williamhockey is one of the least visible founders in tech relative to what he has created. He co-founded Plaid and is now building Column, a software company that owns a bank, and powers Ramp, Wise, Bilt, Mercury, and others. He funded it himself by borrowing against nearly everything he had in Plaid shares, and has never raised any outside capital. His story matters because so much of the value in our industry gets created through exactly this kind of extreme personal risk. He is maniacal about being the best in the world at his thing, and has spent his entire career betting on himself and doing whatever it takes to win. He also spends a lot of time outside the US (in places like Kinshasa) which has given him a rare perch on the power of the US dollar. We discuss: - Why emerging markets are often the most financially innovative - What owning 100% of his company allows him to do that VC-backed founders cannot - Getting margin called and nearly going bankrupt - Why the best founders are specialists - What it takes to be the best in the world at your thing - How Silicon Valley's consensus culture produces consensus founders - How the US dollar functions as an instrument of national security Enjoy! Timestamps: 0:00 Intro 9:19 Emerging Markets 14:03 Silicon Valley's Elite Consensus Problem 16:03 Rejecting the VC Hamster Wheel 21:45 Equity and Liquidity 26:03 Funding a Bank 29:45 The Necessity of Extreme Founder Risk 37:18 Finding Leverage 45:20 Longevity and Profitability in Banking 48:46 Matching Your Capital Structure to Your Business 51:44 The Unseen Power of the US Dollar 1:02:30 How AI Will Transform Legacy Banks 1:09:23 The Kindest Thing

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Hamza
Hamza@HAlfadel·
project hail mary is a masterpiece, must watch
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Hamza
Hamza@HAlfadel·
@dwarkesh_sp Lets go!!! Been excited for this one
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Dwarkesh Patel
Dwarkesh Patel@dwarkesh_sp·
The Terence Tao episode. We begin with the absolutely ingenious and surprising way in which Kepler discovered the laws of planetary motion. People sometimes say that AI will make especially fast progress at scientific discovery because of tight verification loops. But the story of how we discovered the shape of our solar system shows how the verification loop for correct ideas can be decades (or even millennia) long. During this time, what we know today as the better theory can often actually make worse predictions (Copernicus's model of circular orbits around the sun was actually less accurate than Ptolemy's geocentric model). And the reasons it survives this epistemic hell is some mixture of judgment and heuristics that we don’t even understand well enough to actually articulate, much less codify into an RL loop. Hope you enjoy! 0:00:00 – Kepler was a high temperature LLM 0:11:44 – How would we know if there’s a new unifying concept within heaps of AI slop? 0:26:10 – The deductive overhang 0:30:31 – Selection bias in reported AI discoveries 0:46:43 – AI makes papers richer and broader, but not deeper 0:53:00 – If AI solves a problem, can humans get understanding out of it? 0:59:20 – We need a semi-formal language for the way that scientists actually talk to each other 1:09:48 – How Terry uses his time 1:17:05 – Human-AI hybrids will dominate math for a lot longer Look up Dwarkesh Podcast on YouTube, Apple Podcasts, or Spotify.
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Hamza
Hamza@HAlfadel·
One thing I'm looking forward to: AI bringing about more advances in computational biology that puts an end to animal testing once and for all.
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naïve
naïve@usenaive·
Introducing Naive - hire autonomous employees with their own identity. Own compute. Own bank account. Own legal entity. Own email. Own credentials. Own mobile. No humans-in-the-loop. They sign up for tools, pay for services, deploy apps, file documents, and run your entire company. Describe a business. Naive runs it. Reply "Naive" + RT. Get $100 credit for free.
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Blink.new
Blink.new@blinkdotnew·
Introducing Blink Claw - the first platform to hire unlimited AI employees that run your business 24/7. 180+ AI models included. Gmail, Slack, LinkedIn, HubSpot - one-click connect. No API keys, no $600 Mac Mini. Reply "Claw" + RT. Your first agent is on us. ($50 - 200 creds)
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Ivan Burazin
Ivan Burazin@ivanburazin·
Voice, database, web search, sandboxes, file storage, etc., are all separate companies today. But serving the same agent workloads. Directionally, all of these will collapse into one offering. Either through M&A resulting in one company building the full stack or hyperscalers buying them all out. We're essentially building pieces of the agent cloud bundle.
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Hamza
Hamza@HAlfadel·
@j_golebiowski LFM 1.2B is a great model - finetuning it has been a blast
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Jacek Golebiowski
Jacek Golebiowski@j_golebiowski·
We benchmarked 15 small language models across 9 tasks to find out which one you should actually fine-tune. The most surprising result: Liquid AI's LFM2-350M ranked #1 for tunability. 350M parameters, absorbing training signal more effectively than models 20x its size. The entire LFM2 family swept the top 3 spots. No other architecture came close. LFM2-350M: avg rank 2.11 (±0.89) LFM2-1.2B: avg rank 3.44 LFM2.5-1.2B-Instruct: avg rank 4.89 That tight CI means it's consistent across every task type, not just a few lucky benchmarks.
Jacek Golebiowski tweet media
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Adaptive
Adaptive@adaptiveai·
Introducing Adaptive Computer. We put AI inside of an always-on personal computer that it uses to get work done. Schedule agents. Create software. Automate anything. As part of the launch, we’re giving one free month of Adaptive to users. Retweet, like, and comment ‘Adaptive’ to get it.
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Hamza
Hamza@HAlfadel·
autoresearch overnight feels like the days I'd run a bot on runescape overnight
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@joemccann·
This is actually insane. Dude hard-coded a WebAssembly (WASM) interpreter into the weights of a transformer, losslessly. In essence, a computer is running inside a LLM that can actually run computations, not infer or guess a calculation like most do today.
Christos Tzamos@ChristosTzamos

1/4 LLMs solve research grade math problems but struggle with basic calculations. We bridge this gap by turning them to computers. We built a computer INSIDE a transformer that can run programs for millions of steps in seconds solving even the hardest Sudokus with 100% accuracy

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