lukex

6.2K posts

lukex banner
lukex

lukex

@Lukex

co-founder, chief janitor https://t.co/ugjMK4yDEO | venture partner https://t.co/SyPtrlM8Sj

Katılım Ocak 2009
2.7K Takip Edilen3.4K Takipçiler
lukex retweetledi
Spencer Yang
Spencer Yang@spenceryang·
SpaceX may soon become one of the first companies to IPO at a $2T valuation, bringing together SpaceX, xAI, and X. I started my post-college career at Twitter. I watched the platform evolve, grow, struggle, reinvent itself, and even later worked out of its former San Francisco office after it became a co-working space run by BLK71 SF. To mark the moment, I shipped TrillionMarketCap: a live registry of the assets, companies, commodities, and networks large enough to be measured in trillions. Gold. NVIDIA. Apple. Bitcoin. SpaceX.
English
2
1
6
345
lukex retweetledi
Allie K. Miller
Allie K. Miller@alliekmiller·
The most expensive mistake in enterprise AI right now: treating FDEs as your whole transformation plan. Forward deployed engineers (FDEs) are important for custom deployments, but they won’t fix the change management issue most enterprises are facing. It’s likely more the former that Anthropic and OpenAI will continue to prioritize (and hire into the thousands, who knows). Beyond performance and cost, it’s systems integration, ROI, and literal usefulness that drive revenue and stickiness. *However* External FDEs, in my opinion, will not make your company an AI-first company. You can have the sleekest multi-agent orchestrations and still have the majority of your employee base hating AI, avoiding AI, and distrusting leadership decisions on AI. And we already know this because we see this in traditional SaaS too: you can customize the heck out of your Salesforce deployment, but that doesn’t mean your sales team will improve their data hygiene or even attempt to change the way they track and grow with it. Buying a fancier car doesn’t mean you magically learn to drive better overnight. If you’re an enterprise exec and FDEs are sold as the immediate and sole solution to your company transformation woes, walk away. It’s the combination of tech *and* people enablement *and* process reinvention that compounds into actual business outcomes. Large complex enterprises will stall out if they only prioritize the first.
Aaron Levie@levie

Forward deployed engineers, or equivalent, are about to become one of the most in-demand jobs in tech. And one of the most important functions for AI rollouts. Deploying agents is far more technical of a task than most people realize, often far more involved than deploying software. Software generally works the same way every time, and generally for the past few decades has been updated versions of an existing technology or concept (which basically means easier for the enterprise to update their workflows on a newer system). With agents, you’re actually deploying the equivalent of work output within the enterprise. The customer is effectively using you as a professional services provider for a task, which they expect to get solved nearly end-to-end now. This means you need to actually deeply understand the business process as a vendor, and get the customer from the current to the end state seamlessly. Companies need help figuring out which models will work best for their workflows, they need extensive evals setup often, they need change management support for workflows, they need to get their data setup for the agents, and constant tuning of the agentic system for their process. Massive role in tech now. And another example of the kind of highly technical work that AI is creating.

English
81
54
579
114.3K
lukex retweetledi
tae kim
tae kim@firstadopter·
Oh look! Anthropic's entire "we are delaying Mythos" narrative was marketing hogwash. Kudos to FT for confirming what was obvious. Anthropic simply doesn't have the compute. FT: "Multiple people with knowledge of the matter suggested Anthropic was holding back from a wider release until it could reliably serve the model to customers."
tae kim tweet media
tae kim@firstadopter

Everyone should read what's below. This is why actually knowing your stuff instead of naively regurgitating a particular startup's marketing propaganda bullet points is important. I've also included a screenshot of my Substack writeup of Nvidia's Bill Dally and Google's Jeff Dean GTC session that confirms Gavin's analysis.

English
70
206
1.8K
448.1K
lukex retweetledi
Claude-Mem
Claude-Mem@Claude_Memory·
SIXTY THOUSAND (60,000) STARS ⭐️ TODAY ranked amongst legends, #301 and climbing
Claude-Mem tweet mediaClaude-Mem tweet media
English
3
1
26
1.3K
lukex retweetledi
Charly Mwangi
Charly Mwangi@charlythuo·
Just spent a week in China deep diving the general-purpose robotics ecosystem. Key takeaway: while we’re vibe-coding… China is vibe-manufacturing ! A few things that stood out: 1) China has cracked “vibe manufacturing” Startups are spinning up hardware like we spin up code. AGIBot (3 years old) has already built ~10,000 robots. 2) The entire stack is being built in parallel. Every serious robotics company is full-stack: hardware + controls + foundation models. 3) Data factories are real and massive. Hundreds to thousands of people teleoperating robots 24/7 to generate training data. In some cases, the government is literally buying robots, generating data, and selling it back to companies. 4) The supply chain is overwhelming. Foxconn, BYD, LYitech - everyone is plugged into the same dense, hyper-responsive manufacturing base. This is why iteration speed is so high. 5) Structural paradox: Labor is both tailwind and headwind. Cheap, abundant skilled labor powers the supply chain… But it also makes automation harder to justify domestically. → Weak ROI for robotics inside China → Strong incentive to export 6) Hardware is impressive. Intelligence is not (yet). Amazing kinematics—dancing, acrobatics. But limited ability to execute simple instructions reliably. 7) Everyone is moving up the stack Every major CM/ODM is building their own robots—humanoids + wheeled. Today’s suppliers will be tomorrow’s competitors. 8) Dexterity remains unsolved Lots of prototypes. Very few real demos. So what does this mean? Physical AI requires strength in both bits and atoms. Right now: China → dominates atoms (manufacturing, supply chain, scale) US → leads in bits (models, autonomy, software) We are dangerously behind in atoms. If we want to compete, incrementalism won’t cut it. We need to: - Build depth and breadth across the electro-mechanical supply chain - Scale CMs / ODMs / JDMs domestically - Move 100x faster, think 100x bigger on scaling manufacturing infrastructure Hats off to those doing their part to advance domestic manufacturing supply chain - @makematterco, @VulcanForms, @brightmachines, @thebotcompany @gs_ai_ , @MytraUS, @mind_robotics, @tesla_optimus, @atomic_inc, @Senra_Systems, @pathrobotics, @machinalabs_,@figure_robot, @HadrianInc , @agilityrobotics
English
35
138
653
80.5K
lukex retweetledi
Peter Steinberger 🦞
Peter Steinberger 🦞@steipete·
I'm working on character evals and noticed that Claude would constantly pick itself as #1, so I removed the model names from the judge and changed things.
Peter Steinberger 🦞 tweet media
English
81
17
953
109.2K
Martins
Martins@Simplemart17·
@Yuchenj_UW Dug through that leaked source this morning. The 15,000 token system prompt tells you everything. Custom diff formats, specific file editing patterns, agent behavior tuned entirely through prompting. The harness isn't just a gap; it's where the actual product lives.
English
2
0
51
6.9K
Yuchen Jin
Yuchen Jin@Yuchenj_UW·
Beyond raw model capability, the real gap in coding tools is the harness. Now that 500k+ lines of Claude Code are out there, every model lab and AI coding startup, including open-source AI labs, will study it and close that gap fast. SF already has Claude Code source walkthrough meetups lol.
English
97
39
776
87.4K
lukex
lukex@Lukex·
@SamBroner Congrats Sam! Welcome to the builder side
English
0
0
0
67
lukex retweetledi
Gergely Orosz
Gergely Orosz@GergelyOrosz·
This is either brilliant or scary: Anthropic accidentally leaked the TS source code of Claude Code (which is closed source). Repos sharing the source are taken down with DMCA. BUT this repo rewrote the code using Python, and so it violates no copyright & cannot be taken down!
Gergely Orosz tweet media
English
442
1.2K
12.9K
2.2M
lukex retweetledi
Alfred Lin
Alfred Lin@Alfred_Lin·
A CEO from one of our portfolio companies shared this with their team. I’m re-sharing it with their permission, because it resonated and reflects what all founders and CEOs should be communicating. -- We are living through a period of compounding change. And in moments like this, the biggest risk is no longer making the wrong decision. It is moving too slowly while the world moves around you. There are two paths. We can play defense: - Protect what we have - Optimize what works - Wait for clarity It feels safe. It isn’t. Or we can play offense: - Learn faster than the environment changes - Use new tools to solve old problems in better ways - And create entirely new strategies and businesses That’s where the opportunity is. Challenge yourself to do things faster and better than you have ever attempted. Stay uncomfortable. Stay on the front foot.
English
110
429
3K
897.8K
lukex
lukex@Lukex·
1000% agree. you don't win your vertical deploying AI to 10x side quests, while your competitors are deploying AI to 10x main quests
Gergely Orosz@GergelyOrosz

Sage observation from @karrisaarinen (CEO of Linear) It now makes SO MUCH sense why I see a bunch of eng teams rebuilt a SaaS vendor in-house with AI, brag about and feel good They are doing side quests... and they don't even know it. And they are not helping their co win!!

English
3
0
2
313
cookies (🍪,🍪) | 饼妹
cookies (🍪,🍪) | 饼妹@jinglingcookies·
collated > a list of 60 companies building in the agentic commerce space, across these sectors: cards, standards, tooling, identity, credit, checkout execution, discovery, marketplace, policy control > resources to understand and keep up to date with agentic commerce, includes: agentic commerce infra, cards vs stablecoins, traction, analytics comment down below if you would like access
cookies (🍪,🍪) | 饼妹 tweet media
English
45
5
78
15.7K
lukex
lukex@Lukex·
@levie Been thinking and tinkering on this as well! AI will make data more valuable and kill free APIs. Think it splits into 3 groups: internal data (becomes differentiator/moat), frequently accessed data, infrequently accessed data Discoverability will be a big pain point!
English
0
0
2
69
Aaron Levie
Aaron Levie@levie·
There are some pretty wild downstream effects in a world with trillions of agents using the internet and software. One very big one is what happens with agents with budgets and wallets. There are lots of business models that never ended up working out for the human-based internet that all of a sudden start to make economic sense in an agent-based internet. Think of all the proprietary data and research that’s sitting out there right now behind a paywall that a human will never run into. Finance data, medical research, and so on. Most people won’t sign up for a $100 or $1000 subscription for information they need infrequently. The cost is too high. Equally, micropayments for this data rarely worked at scale because the volume was too low to matter. However, now an agent can have a budget for a specific set of research it’s doing, and the agent might pay $0.1 or $1 to access it in a workflow. And now that data may be relevant in 1,000X’s more use-cases than it was before. Similarly, there are many APIs and tools out there on the web that don’t make sense to have a subscription for, but now an agent may interact with for a specific exchange, and it could cost $.01 or $0.1 per transaction. All of a sudden new kinds of software can get built and monetized that would have been uneconomical before. Some new form of commercial open source, essentially. Obviously lots of infrastructure and agreement across the industry is needed for this -and getting discovered by the agent is going to be a whole new class of search and discovery problem- but there are so many potentially interesting new scenarios here.
François Chollet@fchollet

AI agents will soon graduate to fully-fledged economic actors that buy services, compute, and even data in the course of accomplishing high-level goals. 1-2 years before we start seeing this at scale.

English
75
73
625
167.1K
eric
eric@defyneric·
a lot of these agent-to-agent ideas are just way too early conceptually they’re cool, but if you’re building products specifically for agents today there’s basically no TAM the smarter approach right now is building agents for humans first we can’t expect agents to start using other agents until humans are actually using agents in the first place at the end of the day the customer is still a human it reminds me of jeff before hyperliquid, back in 2017 he tried to build a prediction market and it failed the idea wasn’t wrong, it was just too early. then polymarket launched years later and it exploded a lot of these agent to agent ideas are similar. the concept is good, but are founders really able to build, raise money, and run payroll for 5–6 years while the market still doesn’t exist?
English
4
1
27
1.4K
lukex
lukex@Lukex·
@ccatalini AI commoditizing execution makes data more valuable It's why Google is ironically taking an Apple walled garden approach when it comes to Gemini/AI
English
0
0
4
35
lukex retweetledi
Xiaoyin Qu
Xiaoyin Qu@quxiaoyin·
The scariest thing about AI in 2026 isn't some sci-fi scenario. It's watching people you know — people with the same credentials, the same caliber — split into two completely different groups in a matter of months. I've seen it happen firsthand. Stanford grads, ex-Meta engineers, startup founders. Three months ago, they were all roughly at the same level. Now? The divergence is so obvious it's uncomfortable. Some of them got really good at AI. Not just "using ChatGPT" good — fundamentally different in how they think, work, and produce. Their output is compounding. Their depth of insight is compounding. They look like they're playing a different game entirely. Others are still running on the resume they built five years ago. And here's the number that haunts me: 99% of people still use AI at the level of "What's the weather today?" or "What kind of flower is this?" The 1% who figured it out aren't even one group. There's massive variance within them — some are orchestrating AI agents to run entire companies, some use it for research that would take a whole team, some have AI write half their code, some have AI write all of it. The income implications are brutal. If someone uses AI to produce the output of 10,000 people, they're worth 10,000x the salary. Someone who can't figure out a single tool? They might not be worth hiring at all. What really unsettles me is how fast our patience is eroding. The moment we feel someone performs below what AI can do, we don't think "they need training." We think "they're worth zero." Not less. Zero. So the real AI danger isn't AI going rogue. It's the epic, unprecedented amplification of the gap between people — in capability, in income, in relevance. One silver lining: the old hierarchy is broken. People who were once untouchable can now be overtaken by someone who masters AI faster. That door is genuinely open. But if you don't walk through it, you won't just fall behind by a little. You'll become invisible. #AISkillGap #FutureOfWork #ArtificialIntelligence #Productivity
English
46
65
383
85.8K
Silver
Silver@silver_pump·
@dgt10011 ngl i respect his contrarian stance against the hype even if its like shouting into the void lol
English
1
0
2
53
Jeff Park
Jeff Park@dgt10011·
LeCun has been the most aggressive critic of the transformer/LLM consensus for years, and this is his magnum opus "much of real-world sensor data is unpredictable, and generative approaches do not work well." This basically epitomizes his view that real intelligence cannot come from scaling text prediction alone whether he is right or not, im excited to see a another school of thought come to market that in some ways restores agency back to the physical world. Worth "keeping an eye on it" :)
Jeff Park tweet media
AMI Labs@amilabs

Advanced Machine Intelligence (AMI) is building a new breed of AI systems that understand the world, have persistent memory, can reason and plan, and are controllable and safe. We’ve raised a $1.03B (~€890M) round from global investors who believe in our vision of universally intelligent systems centered on world models. This round is co-led by Cathay Innovation, Greycroft, Hiro Capital, HV Capital, and Bezos Expeditions, along with other investors and angels across the world. We are a growing team of researchers and builders, operating in Paris, New York, Montreal and Singapore from day one. Read more: amilabs.xyz AMI - Real world. Real intelligence.

English
14
8
96
24.5K