Telmo Félix

Software engineer from Portugal living in Ireland. I like to build things out of software stacks and Lego pieces.

[Telmo Félix @tisflx]

😂

[Joanna Stern @JoannaStern]

In a review of my household safety evaluations, I identified five incidents in which my child escaped the sandbox, reached the kitchen and gained unauthorized access to the snacks. The incidents occurred 16 months ago but has only now come to my attention.

This post explains what happened, how it happened and why my child is better than your child at everything.

[Telmo Félix @tisflx]

Getting deeper into debt

[Simon Wardley @swardley]

On the question of debt with technical systems & vibe coding

https://t.co/I77TdiESdX

[Telmo Félix @tisflx]

1 year retro. Let's check back in 10.

[Andrej Karpathy @karpathy]

A lot of people quote tweeted this as 1 year anniversary of vibe coding. Some retrospective -

I've had a Twitter account for 17 years now (omg) and I still can't predict my tweet engagement basically at all. This was a shower of thoughts throwaway tweet that I just fired off without thinking but somehow it minted a fitting name at the right moment for something that a lot of people were feeling at the same time, so here we are: vibe coding is now mentioned on my Wikipedia as a major memetic "contribution" and even its article is longer. lol

The one thing I'd add is that at the time, LLM capability was low enough that you'd mostly use vibe coding for fun throwaway projects, demos and explorations. It was good fun and it almost worked. Today (1 year later), programming via LLM agents is increasingly becoming a default workflow for professionals, except with more oversight and scrutiny. The goal is to claim the leverage from the use of agents but without any compromise on the quality of the software. Many people have tried to come up with a better name for this to differentiate it from vibe coding, personally my current favorite "agentic engineering":

- "agentic" because the new default is that you are not writing the code directly 99% of the time, you are orchestrating agents who do and acting as oversight.
- "engineering" to emphasize that there is an art & science and expertise to it. It's something you can learn and become better at, with its own depth of a different kind.

In 2026, we're likely to see continued improvements on both the model layer and the new agent layer. I feel excited about the product of the two and another year of progress.

[Telmo Félix @tisflx]

2026 is going to be real fun

[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.

[Telmo Félix @tisflx]

This. Let's check back in 50 years.

[Eric S. Raymond @esrtweet]

I was writing some code the new-school way yesterday, prompting gpt-4.1 through aider, and for whatever reason my mind flashed back 50 years and the utter freaking enormity of it all crashed in on me like a tidal wave.

And now I want to make you feel that, too.

In 1975 I ran programs by feeding punched cards into a programmable calculator. Actual computers were still giant creatures that lived in glass-walled rooms, though there were rumors from afar of a thing called an Altair.

Unix and C had not yet broken containment from Bell Lab; DOS and the first IBM PC were six years away. The aggregated digital computing capacity of the entire planet was roughly equivalent to a single modern smartphone.

We still used Teletypes as production gear because even video character terminals barely existed yet; pixel-addressable color displays on computers were a science-fiction dream.

We didn't have version control. Public forge sites wouldn't be a thing for 25 years yet. The number of computer games that existed in the world could probably be counted on the fingers of two hands.

Because of all this, I learned to program over the next ten years with tools so primitive that when I talk about them today it sounds like uphill-both-ways sketch comedy.

You may not even be able to imagine what a slow and laborious process programming was then, and how tiny the volume of code we could produce per month was; I have to work to remember it, myself.

Today I call spirits from the vasty deep, conversing with unhuman intelligences and belting out finished programs I would once have considered prohibitively complex to attempt within a single working day.

Fifty years, many generations of hardware technology, from punched cards to AIs that can pass the Turing test...and I'm still here, still coding, still on top of what a software engineer needs to know to get useful work done in the current day. Gotta admit I feel some pride in that!

This meditation isn't supposed to be about me, though. It's about the dizzying, almost unbelievable progress I've lived through and been a part of. If you had told me to predict when I would have a device in my pocket that would give me instant real-time access to most of the world's knowledge, with my own pet homunculi to sift through it for me, I would have been one of the few that wouldn't have said "never" (because I was already a science-fiction fan), but I wouldn't have predicted a date fewer than multiple centuries in the future either.

We've come a hell of a long way, baby. And the fastest part of the ride is only beginning. The Singularity is upon us. Everything I've lived through and learned was just prologue.

[Telmo Félix @tisflx]

😁

[Telmo Félix @tisflx]

😂

[murat 🍥 @mayfer]

🔥 Today we’re excited to announce new funding for `grep` (at a $1.3B valuation) to continue building the foundation of agent observability and text search infrastructure.

grep began as a humble UNIX utility in 1973. Since then, it’s evolved—through recursive innovation and the rise of ripgrep—into a core platform for developers, sysadmins, and agents. Our tools now power engineering and AI teams across @OpenAI, @Anthropic, @Meta, @Cloudflare, @Replit, @NASA, and thousands more.

Over the decades we’ve iterated from grep to `egrep` to `ripgrep`. Our goal has always been to figure out what intelligent agents of the future need to see, filter, and extract—and then build the tools that make that possible.

While our journey is still just beginning, we also want to take a moment to reflect on how the space (and our role in it) has evolved. You can read our reflections and details on this funding milestone here:
https://t.co/um8zFkOqDI

We also share more about the funding that will power our future there. Thank you to @IVP, @Benchmark, @Sequoia, @CapitalG, and the open-source community for their belief in the enduring power of regex.

What excites us most today is what’s next:
grep 5.0 with AI-assisted pattern synthesis
ripgrep Cloud, bringing distributed search to agent clusters
pgrepGPT, an agent-native process discovery layer
And new no-code integrations for autonomous observability pipelines

We’re in the midst of a transformation in computation itself. grep and ripgrep will remain at the core—helping humans and agents alike find what matters, faster.

[Telmo Félix @tisflx]

😣

[Nathan Calvin @_NathanCalvin]

One Tuesday night, as my wife and I sat down for dinner, a sheriff’s deputy knocked on the door to serve me a subpoena from OpenAI.

I held back on talking about it because I didn't want to distract from SB 53, but Newsom just signed the bill so... here's what happened:
🧵

[Telmo Félix @tisflx]

😂

[Deedy @deedydas]

This is the #1 post in r/OpenAI today.

[doomer @uncledoomer]

the engineers who design these machines for weird random physical tasks in manufacturing are so cool. "hey we need a bottle stander upper machine, can you do it?" "sure thing, itll be a big wheel and we'll slap em. we'll just the slap the shit out of em"

[Telmo Félix @tisflx]

Wonder if we should start requiring a Vibe Coding drivers license 😂

[Steve Yegge @Steve_Yegge]

https://t.co/2RwWCjBCXQ

[Farhan Thawar @fnthawar]

The real problem of humanity is the following: We have Paleolithic emotions, medieval institutions and godlike technology

— Edward O Wilson

[David K 🎹 @DavidKPiano]

Why hire a $150k/year dev when you can just pay $200/month for an AI coding agent and $200k/year for a dev to fix its code?

[Telmo Félix @tisflx]

@itsalexvacca Now we should just come up with JPL (json prompt language). Any similarity with some other language is purely coincidental 😅

[Telmo Félix @tisflx]

Just to go on record and to review in some years: LLMs et all are the new compilers.

[Telmo Félix @tisflx]

@karpathy Still it looks we're missing a layer to proper call it engineering... Context is more akin to the registers on a cpu. We're still missing the layer that handles the domain and generates that... A context compiler, maybe?

[Telmo Félix @tisflx]

😅

[Yann LeCun @ylecun]

I don't wanna say 'I told you so', but I told you so.

[Telmo Félix @tisflx]

This. Had brought this same argument a few days ago. 😅

[Erik Meijer @headinthebox]

There is a lot of hyped up excitement about MCP, but I think most people miss the essence of why MCP is a such a BIG thing.

As the fundamental theorem of computer science states "All problems in computer science can be solved by another level of indirection".

That is exactly what MCP does. It introduces a single tool "tools/call" that allows you to call any other tool given its name and arguments. This is similar to reflection in Java or C#.

Next it exposes another tool "tools/list" that allows you to discover all tools that you can call using "tools/call". This is like Class.getMethods() in Java.

This means that only have to implement these two tools "tools/call" and "tools/list" in order to call any tool.

And that reduces the complexity of your "agentic loop" dramatically. So much in fact that the LLM API providers like OpenAI and Anthropic now implement the agentic loop of deciding which tool to call, calling it, and rinse and repeat until the solution is found, for you.

And folks, that is in a nutshell why MCP is such a BIG thing.

[Telmo Félix @tisflx]

And everyone tought it was hard to pick between them....

[Andrej Karpathy @karpathy]

An attempt to explain (current) ChatGPT versions.

I still run into many, many people who don't know that:
- o3 is the obvious best thing for important/hard things. It is a reasoning model that is much stronger than 4o and if you are using ChatGPT professionally and not using o3 you're ngmi.
- 4o is different from o4. Yes I know lol. 4o is a good "daily driver" for many easy-medium questions. o4 is only available as mini for now, and is not as good as o3, and I'm not super sure why it's out right now.

Example basic "router" in my own personal use:
- Any simple query (e.g. "what foods are high in fiber"?) => 4o (about ~40% of my use)
- Any hard/important enough query where I am willing to wait a bit (e.g. "help me understand this tax thing...") => o3 (about ~40% of my use)
- I am vibe coding (e.g. "change this code so that...") => 4.1 (about ~10% of my use)
- I want to deeply understand one topic - I want GPT to go off for 10 minutes, look at many, many links and summarize a topic for me. (e.g. "help me understand the rise and fall of Luminar"). => Deep Research (about ~10% of my use). Note that Deep Research is not a model version to be picked from the model picker (!!!), it is a toggle inside the Tools. Under the hood it is based on o3, but I believe is not fully equivalent of just asking o3 the same query, but I am not sure.

All of this is only within the ChatGPT universe of models. In practice my use is more complicated because I like to bounce between all of ChatGPT, Claude, Gemini, Grok and Perplexity depending on the task and out of research interest.

[Telmo Félix @tisflx]

😂

[Dave W Plummer @davepl1968]

My first machine with a TB of RAM... I'm ready to learn Python!