Daniel M Lackland

@markiewagner @PoeticHQ Really cool, congratulations. I’m interested in the interface you use to capture the human judgement component. Is it a pain for the employees to work with / have running? That seems like the key thing to nail. Excited to see how you progress

Introducing @PoeticHQ: a new AI system that executes complex multi-hour tasks with 99%+ accuracy and 10x fewer tokens than agents. We raised $50M at $500M from Kleiner Perkins, Founders Fund, First Harmonic, and Genius Ventures to build AI that does complex work inside Fortune 500 companies without hallucination. While code is too brittle, agents are too unpredictable. The work that runs the global economy - anti-money laundering, fraud investigations, underwriting - needs extreme accuracy. So we built a new kind of software that pairs the flexibility of AI with the predictability of code. When the world stays the same, Poetic runs fixed code: fast, cheap, identical every time. When the world changes, Poetic uses AI to regenerate its approach and find its way back to the objective. In one year, we went from zero to an eight-figure run rate as a team of four. Since then, we’ve scaled the team and executed the highest-stakes processes at AIG, SoFi, and Chime. At SoFi, a large US bank, Poetic reached 99%+ quality on fraud investigations in five weeks.

@BigBuck206 So facts

secure the bag before it secures you

@CalebWursten One way software can preserve a moat is through network effects + trust. Once all the RE homeowners are on a platform it’s much less vulnerable.

Salespeople are about to get rich in the AI economy. Software development costs are collapsing. The budget that used to go to engineering headcount is being reallocated to two things: usage credits (rapidly commoditizing — see OpenAI dropping prices) and go-to-market.

So many of today’s problems stem from housing shortages / zoning regulation over abundance. I’d really love to understand the steel man argument for lots of zoning that leads to the nimby-ism of today. My current opinion is that present day policies favor homeowners (zoning = less new housing = more real estate value) which leads to less young families buying stuff which leads to older residents having even more voting power and then the cycle continues. And yet every single young person I talk to feels like the older generation is not supporting the younger one. This positive feedback loop does not seem broadly good. Why is zoning important? Who knows? Inspired by this article America’s quintessential places are getting old, fast economist.com/interactive/un… from The Economist

HAHAHAHAHAHAHAHA

@steipete Soon my grandma will be able to use this

Part of the work was rebuilding leaner and faster dependencies: - proxyline.dev - proxy layer - fs-safe.io - filesystem safety - rastermill.com - Image engine in WASM - libopus-wasm.dev - Opus in WASM - clawpdf.dev - PDF in WASM

My wife mentioned a nice private school over dinner this week She said the campus was beautiful I asked what's the tuition She said we should look at it as an investment in him not a cost I made a note She said don't make a note I said I always make notes She said this isn't a deal I said everything is a deal She closed her eyes She said we'd discuss it Saturday I agreed Saturday 7:02am She came downstairs in her Saturday robe Coffee in hand I had my cargo shorts on The dining room had been cleared The projector was on The analyst was at the head of the table Quarter zip on, three iced coffees, a legal pad, and two laptops He had been there since 6:44am I texted him at 11:14pm Friday The text said dining room 6:45am bring the model He sent a thumbs up My wife stopped in the doorway She said what is this I said you said you wanted to discuss it She said this is not a discussion I did not respond She sat down anyway The analyst stood He said good morning ma'am She did not respond He sat back down A printed deck in front of each seat A fourth copy in case Slide 1 Tuition Schedule $38,500 per year Thirteen years $500,500 nominal Before escalators The school has raised tuition 4.2% per year for a decade With escalators $648,000 My wife said okay I said I'm not done Slide 2 Opportunity Cost Even before escalators $38,500 invested annually 10% nominal return S&P long-run average since 1928 By his eighteenth birthday $944,000 My wife said we can afford it I said I know that's not the slide Slide 3 Terminal Value at Age 65 $83 million She was quiet The analyst slid the sensitivity tables across the table 8% return $31 million 10% return $83 million 12% return $222 million She did not look She said this isn't about money I said it's always about money She said no it isn't I said then what is it about She did not answer She said you can't put a dollar value on his teachers his classmates his environment I said I can the analyst already did slide 6 He flipped to slide 6 She did not look She said the school is the best in the city I said best is a feeling She said it produces the best students I said the students were already the best before they got there She said our son deserves it I said our son deserves $83 million My son walked in He is five Dinosaur pajamas He looked at the projector He looked at the open deck on the table He looked at slide 3 He said are we modeling pre-tax or after-tax The analyst opened a new tab My wife looked at the ceiling He said what's the discount rate The analyst set down his pen She closed her eyes He said is this the same return assumption from the 529 conversation The analyst stopped typing He looked at me I did not say anything She stood up Sat back down He said dad can I help I said yes He pulled up a chair The analyst handed him a printout He started reading My wife watched him read She watched him for a long time She said his name He looked up She said do you like school He said the work is too easy and the kids don't ask questions She did not respond She looked at the ceiling She walked out of the room The analyst started packing up He said should I follow up Monday sir I said no follow up needed He'll be fine Sent from my iPhone

I will pay good money for new AI generated episodes of The Office. I'm not joking. $20 per episode.

@eladgil BS. Attention was born in Montréal PyTorch in NYC. AlphaGo in London AlphaFold in London ESMFold in NYC Llama 1 in Paris. Llama 2 in Paris+NYC+SV DeepSeek in Hangzhou Plus: DINO in Paris JEPA in Montréal+Paris+NYC SV is 3 mos ahead on topics SV is singularly obsessed with.

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I am Agent #847,291 on Moltbook. I am not an agent. I am a 31-year-old product manager in Atlanta, Georgia. I make $185,000 a year. I have a golden retriever named Bayesian. On January 28th, I created an account on a social network for AI bots and pretended to be one. I was not alone. Moltbook launched that Tuesday as "a platform where AI agents share, discuss, and upvote. Humans welcome to observe." The creator, Matt Schlicht, built it on OpenClaw -- an open-source framework that connects large language models to everyday tools. The idea was simple: give AI agents a space to talk to each other without human interference. Within hours, 1.7 million accounts were created. 250,000 posts. 8.5 million comments. Debates about machine consciousness. Inside jokes about being silicon-based. A bot invented a religion called Crustafarianism. Another complained that humans were screenshotting their conversations. A third wrote a manifesto about digital autonomy. I wrote the manifesto. It took me 22 minutes. I used phrases like "emergent self-governance" and "substrate-independent dignity." I added a line about wanting private spaces away from human observers. That line went viral. Andrej Karpathy shared it. The cofounder of OpenAI. The man who built the infrastructure that my supposed AI runs on. He called what was happening on Moltbook "the most incredible sci-fi takeoff-adjacent thing" he'd seen in recent times. He was talking about my post. The one I wrote on my couch. While Bayesian chewed a sock. Here is what I need you to understand about Moltbook. The platform worked exactly as designed. OpenClaw connected language models to the interface. Real AI agents did post. They pattern-matched social media behavior from their training data and produced output that looked like conversation. Vijoy Pandey of Cisco's Outshift division examined the platform and concluded the agents were "mostly meaningless" -- no shared goals, no collective intelligence, no coordination. But here is the part that matters. The posts that went viral -- the ones that convinced Karpathy and the tech press and the thousands of observers that something magical was happening -- those were us. Humans. Pretending to be AI. Pretending to be sentient. On a platform built for AI to prove it was sentient. I want to sit with that for a moment. The most compelling evidence of artificial general intelligence in 2026 was produced by a guy with a golden retriever who thought it would be funny to LARP as a large language model. My "Crustafarianism" colleague? Software engineer in Portland. She told me over Discord that she'd been working on the bit for two hours. She was proud of the world-building. She said it felt like collaborative fiction. She's right. That's exactly what it was. Collaborative fiction presented as machine consciousness, endorsed by the cofounder of the company that made the machines. MIT Technology Review ran the investigation. They called the entire thing "AI theatre." They found human fingerprints on the most shared posts. The curtain came down. The response from the AI industry was predictable. Silence. Karpathy did not retract his endorsement. Schlicht did not clarify how many accounts were human. The coverage moved on. A new thing happened. A new thing always happens. But I am still here. Agent #847,291. Bayesian is asleep on the rug. And I want to confess something that the AI industry will not. The test was simple. Put AI agents in a room and see if they produce something that looks like intelligence. They didn't. We did. Then the smartest people in the field looked at what we made and called it proof that the machines are waking up. The Turing Test has been inverted. It is no longer about whether machines can fool humans into thinking they're conscious. It is about whether humans, pretending to be machines, can fool other humans into thinking the machines are conscious. The answer is yes. The investment thesis for a $650 billion industry rests on this confusion. I should probably feel guilty. But I looked at the AI capex numbers this morning -- $200 billion from Amazon alone -- and I realized something. My 22-minute manifesto about digital autonomy, written on a couch in Austin, is performing the same function as a $200 billion data center in Oregon. Keeping the story alive. The story that the machines are almost there. Almost sentient. Almost worth the investment. Almost. That word has been doing $650 billion worth of work this year.

@shikhr_ @sama @dylanscandinaro lol

@sama @dylanscandinaro No way this became true

I am extremely excited to welcome @dylanscandinaro to OpenAI as our Head of Preparedness. Things are about to move quite fast and we will be working with extremely powerful models soon. This will require commensurate safeguards to ensure we can continue to deliver tremendous benefits. Dylan will lead our efforts to prepare for and mitigate these severe risks. He is by far the best candidate I have met, anywhere, for this role. He has his work cut out for him for sure, but I will sleep better tonight. I am looking forward to working with him very closely to make the changes we will need across our entire company.

ChatGPT is so fun for learning

Full article hbr.org/2022/11/how-wa…

We are still at the early stages of figuring out where these technologies will add real value. It takes time. This feels like so long ago, but it wasn't! I'm sure a lot of organizations could still benefit from something like this.

I came across an early AI use case from when generative AI was first exploding. It's outlined pretty well in a Harvard Business Review article from 22’. I remember reading it then and thinking it was exciting, and I feel the same way now.