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justine
202 posts
justine
@justinemach_
growth eng @browserbase cs+history @columbia @columbiaentrep
Katılım Mart 2025
296 Takip Edilen2.3K Takipçiler
@_shubhankar @browserbase browsers on browsers on browsers on browsers
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i built a site to show the core primitives powering @browserbase
search, fetch, browser, and when to reach for each
check it out! browserbase.com/demo
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I sat 20ft away while she filmed this entire thing and I have to say that I am intimidated by the hutzpah on this gal she puts me to shame by a mile - a freakin go getter
justine@justinemach_
i built a site to show the core primitives powering @browserbase search, fetch, browser, and when to reach for each check it out! browserbase.com/demo
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Had lots of fun speaking with @PaulKlein about all things forward deployed engineering, @DecagonAI, and more. Thanks for the chat :)
Paul Klein IV@pk_iv
"In Decagon's world, forward deployed engineering is product engineering. There's no difference." I sat down with @sunnyrekhi, CTO of Deployed Engineering at @DecagonAI, at our @aiDotEngineer booth to get into what it actually takes to bring AI customer service to the enterprise. 0:00 Decagon's 24/7 AI customer service agent, replacing "press 1 for billing" 3:55 Why forward deployed engineering is just product engineering at Decagon 6:29 What people get wrong about the product: it's a lot more than a chatbot 7:14 Why the customer has to own the agent, not a patchwork 9:20 Simulations: stress-testing the agent with real conversations before it ships
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justine retweetledi
"In Decagon's world, forward deployed engineering is product engineering. There's no difference."
I sat down with @sunnyrekhi, CTO of Deployed Engineering at @DecagonAI, at our @aiDotEngineer booth to get into what it actually takes to bring AI customer service to the enterprise.
0:00 Decagon's 24/7 AI customer service agent, replacing "press 1 for billing"
3:55 Why forward deployed engineering is just product engineering at Decagon
6:29 What people get wrong about the product: it's a lot more than a chatbot
7:14 Why the customer has to own the agent, not a patchwork
9:20 Simulations: stress-testing the agent with real conversations before it ships
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very interesting take on the computational efficiency of open source models
kimi k3 just showed that open-weights frontier models can reach sota performance, but if it burns 50-70% more compute per task then maybe the frontier that matters is the cost-efficient one
Gavin Baker@GavinSBaker
Kimi K3 may be an important inflection point for AI. Potentially negative for Anthropic and OpenAI while being net positive for essentially every other company in the world. I mean that very literally. Although the real “Sputnik moment” would be an open-source frontier model that was also token efficient unlike Kimi K3 which is 50-70% more expensive to run than GPT 5.6 per Artificial Analysis. Rationale: A world where there are only 2-3 dominant frontier labs with 90% inference margins is net negative for every other layer while being awesome for those 2-3 labs. Those labs would become monopsonies for power, data centers, semiconductors and hyperscalers and would obviously vertically integrate over time into all those layers while also completely subsuming the application/software layers. Anything that lowers margins and increases competition at the model layer is good for every other AI layer: power, semiconductors, hyperscalers, neoclouds and yes even software. This is why Jensen is so supportive of open-source. An open-source model requires the *exact* same amount of compute to run as a closed frontier model of similar size and architecture. Kimi K3 is roughly the same price as GPT 5.6 Terra on a per token basis, which actually suggests that it is less computationally efficient as I am sure that GPT 5.6 is priced to a higher margin than K3. And given that K3 is a token wastrel, i.e. token inefficient, it is significantly more expensive per task than GPT 5.6 and Grok 4.5, which are much more token efficient. Cost per token and token efficiency (i.e. intelligence density per token) are the drivers of intelligence per unit of cost. The winning AI companies will be those that offer the most intelligence per $ over time. Lower margin % at the model layer = more margin $ at every part of the infrastructure layer and is a godsend for software. This can happen either through open-source models like K3 at the frontier *or* having a vertically integrated model company like Meta, SpaceX or Google at the frontier. Both outcomes result in a lower margin % at the model layer as vertically integrated model companies don’t really care where the margin $ come from. This is why it was so painful for OpenAI and Anthropic when Google was right there with them from a model competitiveness perspective and why Grok 4.5 and Muse 1.1 were just as important as Kimi K3. The reason Kimi K3 is only *potentially* negative for Anthropic and OpenAI is 1) the @ericvishria point that the Claude and ChatGPT products and harnesses may be more important than their models today and 2) the hypothesis that they have much more advanced model checkpoints internally that are already being used for RSI. In the latter scenario, reaching RSI even a few months ahead of other labs might be enough to cement a permanent lead. Time will tell on both points. And likely fairly quickly. Caveat would be that since Kimi K3 is not token efficient and thereby actually more expensive than ChatGPT 5.6, we may need to see a more token efficient open-source model at the frontier or see Grok 5/Composer 4/Muse 2 at multiple points on the Pareto frontier for this potential risk to Anthropic and OpenAI to play out. And I am sure they will both vertically integrate as quickly as possible while continuing the product/harness strength they have shown over the last 8 months.
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@perryzjia @Stefania_druga @SakanaAILabs @browserbase @aiDotEngineer @MIT @GoogleDeepMind ooh i like this.
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@justinemach_ @Stefania_druga @SakanaAILabs @browserbase @aiDotEngineer @MIT @GoogleDeepMind may I suggest "human in the lead"?
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"to me, it's not about the human in the loop, but the human at the center."
i sat down with @Stefania_druga, staff research scientist @SakanaAILabs, at our @browserbase booth at @aiDotEngineer.
after @MIT and @GoogleDeepMind, stefania recently joined sakana's recursive self-improvement (rsi) lab in tokyo to build self-improving AI for the next frontier.
we got into:
- catastrophic forgetting in day-long agent research runs, and how memory-first harnesses help
- sovereign AI and open-weights: building on models you control instead of locking into one provider
- why computer use still frustrates people even as models get smarter
- the domains ripe for self-improving research agents: math, genomics, rare conditions
- my fav moment at the end: what tasks are better left human, and what are we going to do as humans after everything is automated?
with open frontier models arriving fast, @Stefania_druga's case for sovereign AI only gets stronger.
loved this conversation. thanks for joining us at the @browserbase booth!
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@Stefania_druga @tweetsatpreet @SakanaAILabs @browserbase @aiDotEngineer @MIT @GoogleDeepMind appreciate the support!
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@tweetsatpreet @MachJustine @SakanaAILabs @browserbase @aiDotEngineer @MIT @GoogleDeepMind I agree! You should keep doing this Justine!
Seattle, WA 🇺🇸 English
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@MachJustine @Stefania_druga @SakanaAILabs @browserbase @aiDotEngineer @MIT @GoogleDeepMind love this!!!
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@tweetsatpreet @Stefania_druga @SakanaAILabs @browserbase @aiDotEngineer @MIT @GoogleDeepMind thank you sat!
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@MachJustine @Stefania_druga @SakanaAILabs @browserbase @aiDotEngineer @MIT @GoogleDeepMind Cool work @Stefania_druga! And great energy @MachJustine, I hope you grow this into a podcast or something similar.
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People often ask me: how are teams using @browserbase to power their workflows?
I’ve met teams building things I never would’ve imagined a browser agent could do:
- Receipts that file themselves,
- Vendor research done in minutes,
- Demos that close million-dollar deals, and so much more!
If you're building an agent that needs to actually do things on the web, come talk to me!
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My bet: @thinkymachines will soon make more money than @AnthropicAI. Not by winning the race to build one standardized frontier model. By becoming the Palantir FDE for enterprise custom models.
The playbook:
1. Release the best American open-weight model.
2. Drive widespread enterprise adoption.
3. Charge the largest companies 7–9 figures to post-train and run custom models behind their own firewall.
The model rests on three bets:
1. Large enterprises will increasingly demand their own models with their own data, and this is how they differentiate and win.
2. Enterprises won’t need just one model. They’ll continuously need new models for different workflows, departments, and proprietary datasets. That creates extremely sticky, recurring revenue.
3. Autoresearch will make custom model development increasingly scalable. Tinker can become the interface enterprises use to post-train their own models—with @thinkymachines providing the expertise and infrastructure behind it. FDE, infra, everything, huge contracts.
4. Eventually, maybe everyone wants their OWN model, and autoresearch and training inside tinker on top of @thinkymachines's base model will make it happen.
Meanwhile, Henry-ford-styled, standardized models will makes no margins. OpenAI and Anthropic will have their API margins squeezed by Deepseek/GLM/Grok/Meta etc, and their consumer subscriptions are loss centers.
The fat margin will move to customization: proprietary data, post-training, evals, deployment, and infrastructure.
If this thesis is right, @thinkymachines isn’t building just another frontier lab. It’s building the highest-value layer between frontier research and enterprise model ownership.
Turns out, the best business model for enterprise is NOT to sell commodity API access. Sell them their own models.
I’m extremely bullish on this approach.
@miramurati may be the most commercially savvy frontier-lab leader. I have to admit it.
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@MachJustine feature request can you include the timestamps of the parts of the convo you mention in your recaps
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@liangsays @Stefania_druga @SakanaAILabs @browserbase @aiDotEngineer @MIT @GoogleDeepMind yapping until the singularity comes for me
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@MachJustine @Stefania_druga @SakanaAILabs @browserbase @aiDotEngineer @MIT @GoogleDeepMind amazing!
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