Justin McCarthy

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Justin McCarthy

Justin McCarthy

@BuiltByJustin

builder, founder, engineer, AI && CTO @StrongDM

Redwood City, CA Katılım Haziran 2009
73 Takip Edilen302 Takipçiler
Justin McCarthy retweetledi
François Chollet
François Chollet@fchollet·
Sufficiently advanced agentic coding is essentially machine learning: the engineer sets up the optimization goal as well as some constraints on the search space (the spec and its tests), then an optimization process (coding agents) iterates until the goal is reached. The result is a blackbox model (the generated codebase): an artifact that performs the task, that you deploy without ever inspecting its internal logic, just as we ignore individual weights in a neural network. This implies that all classic issues encountered in ML will soon become problems for agentic coding: overfitting to the spec, Clever Hans shortcuts that don't generalize outside the tests, data leakage, concept drift, etc. I would also ask: what will be the Keras of agentic coding? What will be the optimal set of high-level abstractions that allow humans to steer codebase 'training' with minimal cognitive overhead?
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Justin McCarthy
Justin McCarthy@BuiltByJustin·
We're now helping others build factories. The first exercise is to build an Attractor. I just completed one in C. The code in src/llm really illustrates how small & straightforward a coding agent can be. github.com/jmccarthy/attr…
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Justin McCarthy
Justin McCarthy@BuiltByJustin·
@brunotorious @strongdm awesome Bruno - added to our table of implementations - keep it going! #community" target="_blank" rel="nofollow noopener">factory.strongdm.ai/products/attra…
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Justin McCarthy retweetledi
Ethan Mollick
Ethan Mollick@emollick·
A genuinely radical approach to software development with AI, without any human intervention. Even if this approach doesn’t work for many cases, I think we need more leapfrogging visions for how to redo processes with AI: factory.strongdm.ai See also: danshapiro.com/blog/2026/01/t…
Ethan Mollick tweet media
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Andrew McCalip
Andrew McCalip@andrewmccalip·
The power of MoltBot is getting people to update their biases and their world model. It's just the activation energy to cause the bit to flip in people's heads that we're in a takeoff. All this is happening with ~500 pages of context window and static weights. Moltbook isn't technically more impressive than the coding work we're all doing already, but it's more of a performative art piece that captures people's attentions. I'm maintaining that the last piece for true AGI is continual learning. The ability to distill context windows into permanent weight updates on the fly is going to cause all this to go absolutely exponential. We're so close.
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Justin McCarthy
Justin McCarthy@BuiltByJustin·
our job is to illuminate the terrain. when the agent wakes up, it needs the correct path to be obvious. we don't have great control over the terrain, but we do control how it's lit - that's the engineering part of context engineering
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Justin McCarthy
Justin McCarthy@BuiltByJustin·
@ibuildthecloud Let the model reach for where it thinks that code should probably be, rather than documenting why you put it elsewhere
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Darren Shepherd
Darren Shepherd@ibuildthecloud·
I'm finding it's way easier for AI to maintain/enhance a greenfield AI app. Before you say, duh, what I'm saying is that the whole app is "AI logical." These are not simple apps I'm writing. But the consistency helps with ensuring quality. I could see a trend of rewriting apps to be AI managed apps. Instead of trying to maintain existing code bases.
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rahul
rahul@rahulgs·
yes things are changing fast, but also I see companies (even faang) way behind the frontier for no reason. you are guaranteed to lose if you fall behind. the no unforced-errors ai leader playbook: For your team: - use coding agents. give all engineers their pick of harnesses, models, background agents: Claude code, Cursor, Devin, with closed/open models. Hearing Meta engineers are forced to use Llama 4. Opus 4.5 is the baseline now. - give your agents tools to ALL dev tooling: Linear, GitHub, Datadog, Sentry, any Internal tooling. If agents are being held back because of lack of context that’s your fault. - invest in your codebase specific agent docs. stop saying “doesn’t do X well”. If that’s an issue, try better prompting, agents.md, linting, and code rules. Tell it how you want things. Every manual edit you make is an opportunity for agent.md improvement - invest in robust background agent infra - get a full development stack working on VM/sandboxes. yes it’s hard to set up but it will be worth it, your engineers can run multiple in parallel. Code review will be the bottleneck soon. - figure out security issues. stop being risk averse and do what is needed to unblock access to tools. in your product: - always use the latest generation models in your features (move things off of last gen models asap, unless robust evals indicate otherwise). Requires changes every 1-2 weeks - eg: GitHub copilot mobile still offers code review with gpt 4.1 and Sonnet 3.5 @jaredpalmer. You are leaving money on the table by being on Sonnet 4, or gpt 4o - Use embedding semantic search instead of fuzzy search. Any general embedding model will do better than Levenshtein / fuzzy heuristics. - leave no form unfilled. use structured outputs and whatever context you have on the user to do a best-effort pre-fill - allow unstructured inputs on all product surfaces - must accept freeform text and documents. Forms are dead. - custom finetuning is dead. Stop wasting time on it. Frontier is moving too fast to invest 8 weeks into finetuning. Costs are dropping too quickly for price to matter. Better prompting will take you very far and this will only become more true as instruction following improves - build evals to make quick model-upgrade decisions. they don’t need to be perfect but at least need to allow you to compare models relative to each other. most decisions become clear on a Pareto cost vs benchmark perf plot - encourage all engineers to build with ai: build primitives to call models from all code bases / models: structured output, semantic similarity endpoints, sandbox code execution. etc What else am I missing?
Andrej Karpathy@karpathy

I've never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between. I have a sense that I could be 10X more powerful if I just properly string together what has become available over the last ~year and a failure to claim the boost feels decidedly like skill issue. There's a new programmable layer of abstraction to master (in addition to the usual layers below) involving agents, subagents, their prompts, contexts, memory, modes, permissions, tools, plugins, skills, hooks, MCP, LSP, slash commands, workflows, IDE integrations, and a need to build an all-encompassing mental model for strengths and pitfalls of fundamentally stochastic, fallible, unintelligible and changing entities suddenly intermingled with what used to be good old fashioned engineering. Clearly some powerful alien tool was handed around except it comes with no manual and everyone has to figure out how to hold it and operate it, while the resulting magnitude 9 earthquake is rocking the profession. Roll up your sleeves to not fall behind.

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Aaron Levie
Aaron Levie@levie·
To get the full benefit of AI agents you often need to change your underlying workflows, and keep up with a very fast moving AI space. Because of this, there are at least 2 entirely new categories of business models that will emerge around the software companies that build agents. 1. The services firm that implements AI agents in existing companies. As enterprises look to deploy AI agents across all forms of work, it’s not possible for every company to figure out how to do this on their own. Most companies don’t have the IT teams to deliver on this, so there will be entirely new system integrators that emerge to help companies redesign their workflows, implement the tech, drive the change management, and keep the AI agents up to date for the organization. But what’s super interesting is that because AI agents span almost every single line of business, these will not just be the classic system integrators whose primary focus is on IT systems. The system integrators will have to be domain experts at many different types of job functions, from marketing and legal to healthcare and coding. 2. New agency or firm that forms from the ground up to take advantage of the leverage of agents. Lots of companies will take too long to transform themselves with AI, so there will be an all new crop of companies that start from scratch the capture the gains. These services firms and agencies will use the technology themselves to offer cheaper, faster, or better quality of service to a broader range of clients than was possible before. This will be the new law firm that uses AI to change the business model of, marketing agencies that can support high quality campaigns for smaller size companies, engineering shops that can take on bigger project work at a lower cost, and so on. In all there are going to be lots of new forms of businesses that will emerge as a result of AI agents because of how different working with agents can be.
GREG ISENBERG@gregisenberg

This feels directionally right. An agency comes in for a few weeks, maps how work actually flows, and installs claude skills/agents that handle reporting, follow-ups, checks, and coordination. That replaces work spread across a few roles that might cost $250k–$400k a year. The company pays once for the setup, keeps the system, and only brings the agency back when something needs tuning. Of course agencies don’t go away since human judgment is always needed, but a growing share of what clients pay for shifts toward skills and agents that run inside the business. I keep coming back to this idea and it keeps making more sense.

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Jeff Lindsay
Jeff Lindsay@progrium·
apptron is the only pure browser environment to run the complete go toolchain, capable of cross compiling to over 40 platforms
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Aaron Levie
Aaron Levie@levie·
We will soon get to a point, as AI model progress continues, that almost any time something doesn’t work with an AI agent in a reasonably sized task, you will be able to point to a lack of the right information that the agent had access to. This is why context engineering is the future. Basically you’re reverse engineering what an insanely smart human, would need to perform a particular task. The caveat is this super smart person is an expert at almost any type of field of work, but one day they’re a lawyer at a Fortune 500 and the next day they’re an engineer at a startup. And they forget what they did between each task. And they can only keep track of one medium-sized thing at a time. Super fun challenge. This means they need a ton of context - but not too much to get confused - about what they’re doing and why. So the job then is to try and build the system or set of systems necessary to deliver that data to the model as efficiently and quickly as possible. This is why so much time is just going to straight into search and retrieval systems, heuristics for ranking information, system prompts, ways of keeping track of the work that’s being done to save context window space, and so on. One cool thing, though, is that unlike a person, this agent can process vastly more data at once, so all of a sudden you can apply more compute to the problem than would otherwise be helpful with people. An insanely fun time right now to be building agents.
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David Sinclair
David Sinclair@davidasinclair·
Yes it does
Patrick Malone, MD PhD@patricksmalone

biotech needs its own david sacks in reflecting on this past year, one thing has become increasingly obvious to me: biotech desperately needs a public champion. someone who can translate scientific progress into policy, coordinate the industry’s scattered voices into a coherent agenda, and frame biotech as a strategic national priority rather than a niche technical field. this is perhaps the biggest structural weakness facing our industry. watching the policy momentum behind AI and crypto has been frustrating. these sectors have moved quickly not just because the technology is advancing, but because people like david sacks have created a central organizing force. they’ve built a coherent narrative, rallied founders and investors, and focused the tech industry’s efforts in washington. biotech has no equivalent. what makes this more frustrating is that the rationale driving urgency in AI policy applies almost word-for-word to biotech: competition with china. national security. domestic manufacturing capacity. strategic dependence on foreign supply chains. you could literally replace “AI” or “rare earths” with “biotech” in many of the recent executive orders, and the logic would hold perfectly. these should be obvious, bipartisan reasons to invest in and accelerate the biotech ecosystem. yet the case isn’t being made with the same clarity or force. part of the problem is a PR failure. most policymakers don’t understand that biotech ≠ pharma. biotech startups are the innovators; pharma is the innovation buyer. but in washington, these groups get conflated. early-stage biotech gets pulled into the same policy debates as multibillion-dollar incumbents, and the result is predictable: the people doing the actual innovation are not represented. another issue is fragmentation. AI and crypto accelerated because the community acted like a movement. there was a center of gravity pulling together founders, operators, investors, and policymakers. biotech, by contrast, is spread across academic labs, NIH, the FDA, startups, pharma, state governments, and a long tail of investors. large pharma and small biotech don't often have the same priorities and incentives. there is no unifying node that turns these pieces into a coherent whole. biotech doesn’t just need more innovation; it needs coordination. it needs someone who can articulate why this industry matters, make the geopolitical case, advocate for regulatory clarity, and translate between science and washington. it needs someone who can build a narrative around biotech as a strategic national asset rather than a niche technical field. biotech needs its david sacks: a movement builder, a policy champion, a narrative architect. until someone steps into that role, the industry will continue to produce world-class science while punching far below its weight in culture, policy, and national strategy.

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James Cham
James Cham@jamescham·
The race to create the knowledge worker version of Claude Code.
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Justin McCarthy
Justin McCarthy@BuiltByJustin·
We use X-ray/CT for turbofan blade QC. If it improves confidence, why not use it for every fastener on the Golden Gate bridge? Or my Honda? With a high token budget ("mass tokens"), we can use math to achieve the same outcome as direct inspection: sundaylettersfromsam.substack.com/p/we-can-build…
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