Matt Sanchez

CTO | Board Director | Entrepreneur

[Rob Thomas @robdthomas]

https://t.co/6H0KO3JDA8

[Andrej Karpathy @karpathy]

A few random notes from claude coding quite a bit last few weeks.

Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent.

IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits.

Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased.

Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion.

Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage.

Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building.

Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it.

Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements.

Questions. A few of the questions on my mind:
- What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*.
- Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro).
- What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music?
- How much of society is bottlenecked by digital knowledge work?

TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.

[Andrej Karpathy @karpathy]

# On the "hallucination problem"

I always struggle a bit with I'm asked about the "hallucination problem" in LLMs. Because, in some sense, hallucination is all LLMs do. They are dream machines.

We direct their dreams with prompts. The prompts start the dream, and based on the LLM's hazy recollection of its training documents, most of the time the result goes someplace useful.

It's only when the dreams go into deemed factually incorrect territory that we label it a "hallucination". It looks like a bug, but it's just the LLM doing what it always does.

At the other end of the extreme consider a search engine. It takes the prompt and just returns one of the most similar "training documents" it has in its database, verbatim. You could say that this search engine has a "creativity problem" - it will never respond with something new. An LLM is 100% dreaming and has the hallucination problem. A search engine is 0% dreaming and has the creativity problem.

All that said, I realize that what people *actually* mean is they don't want an LLM Assistant (a product like ChatGPT etc.) to hallucinate. An LLM Assistant is a lot more complex system than just the LLM itself, even if one is at the heart of it. There are many ways to mitigate hallcuinations in these systems - using Retrieval Augmented Generation (RAG) to more strongly anchor the dreams in real data through in-context learning is maybe the most common one. Disagreements between multiple samples, reflection, verification chains. Decoding uncertainty from activations. Tool use. All an active and very interesting areas of research.

TLDR I know I'm being super pedantic but the LLM has no "hallucination problem". Hallucination is not a bug, it is LLM's greatest feature. The LLM Assistant has a hallucination problem, and we should fix it.

</rant> Okay I feel much better now :)

[Matt Sanchez @MattSanchez]

Looking forward to tonight's event at the @AustinForum
https://t.co/2Xq05u8UL2 #ResponsibleAI

[CognitiveScale @CognitiveScale]

Cortex #Certifai is now available on #aws marketplace. Gain visibility and control over automated decisions by automatically detecting and scoring model vulnerabilities including performance, data drift, bias, explainability, and robustness. Link below!
https://t.co/sTsbupZFvE

[CognitiveScale @CognitiveScale]

What does it mean for us to use #trustedAI in #healthcare, an industry facing many challenges, especially at the current moment with #covid19? The wise words of our Jeffrey Eyestone about avoiding the #risk that black box solutions may create. #Certifai
https://t.co/1oMq8Vn4tA

[CognitiveScale @CognitiveScale]

While #AI may be a risk, it's also the perfect solution to managing risk. Read more from our @manojsaxena on the pragmatic mindset we must adopt to create a world benefiting from #EthicalAI. #Certifai #YouandAIWillChangetheWorld #TrustAsAService
https://t.co/hgPthdF2WD

[Dr Jette Henderson @ejette]

Presenting joint work between @CognitiveScale and @UTAustin that delivers realistic counterfactual explanations to users and also allows model developers to study fairness and robustness at #SafeML #ICLR2019 please stop by!

[CognitiveScale @CognitiveScale]

Want to stay ahead of your competition? Register for "The State of AI" webinar now to join our Dr. Joydeep Ghosh and @_ganeshp as they discuss new research &amp; technology breakthroughs in #AI, the current talent pool, and hypes &amp; realities of the AI market. https://t.co/3YnGAmELEy

[Matt Sanchez @MattSanchez]

Easy Kubernetes and free t-shirts #DOK8s https://t.co/JfumLjM6MT

[Ashley Casovan @AshleyCasovan]

Yes! @JStanghini and @TransportCan Look! Your work in real life! Thanks for working with us to do a deep dive and mature the Algorithmic Impact Assessment! https://t.co/VSexhQUYWv amazing work @CognitiveScale to make this become a reality! #GCDigital #Cognite2018 @AlexBenay

[Matt Sanchez @MattSanchez]

@AmericanAir Upgrade came through Monday, plane didn’t change, they just oversold it. I find out at the gate that my upgrade is downgraded - terrible customer experience.

[Matt Sanchez @MattSanchez]

“You’ve been downgraded” says the gate agent @AmericanAir. I’m fine not getting an upgrade, but to upgrade, then downgrade? Really?!??

[Cognilytica @cognilytica]

8 ways in which #AI is improving the overall #banking experience and impacting the industry

https://t.co/9sMPsv3iH1

#infographic #Artificialintelligence

CC: @CognitiveScale @manojsaxena @MattSanchez

[CognitiveScale @CognitiveScale]

Happy #NationalTequilaDay! Cheers from us at CognitiveScale!

[CognitiveScale @CognitiveScale]

Thanks for the mention, @Forbes! We are proud to be recognized as 2018 Technology Pioneers by @wef. This is only the beginning. #EnterpriseAI #ResponsibleAI https://t.co/T2jbCdamj3

[CognitiveScale @CognitiveScale]

Want to be part of a team that was recognized as Technology Pioneer by the @wef and is positively impacting the world through #AI? Now's your chance because we are hiring! Check out open positions across Product Management, Engineering, Marketing: https://t.co/AYj5sOjIRj

[Claus Torp Jensen @ClausTorpJensen]

Great piece about behaviors that define Great Leaders - all eleven behaviors are excellent advice, in particular "Listen to your heart" https://t.co/Uo7fXy5AaT

[Matt Sanchez @MattSanchez]

@jeffrschneider Yes, it’s a little hard to understand without context. More information is forthcoming soon.