An analog for you: I started in software pre-cloud, with a primary focus on web technologies, but at various times learned c/c++/asm/compilers/operating systems/algorithms. I audited many classes, but one instructive one was a course called nand2tetris (going from nand gates to hardware simulated CPU to peripherals to operating system to programming languages and compilers to application layer). Understanding the global through lines in computer science give you immense power.

Does my work as a CTO mean I use this information all the time? Absolutely not. Did I benefit from figuring out how computers work generally throughout literally my entire career? Absolutely yes.

I am actually relatively AI-pilled compared to the average Hacker News reader, but I am against outsourcing fundamental understanding to AI in all forms. Does that mean I write code by hand often anymore? Very infrequently.

buts its a refreshing that there is an initial list of half baked projects, i suppose meant to evoke horror at the untidiness and wasted time. but honestly each of those projects sound cool as hell. not necessarily durable - but who cares. i’d argue there is a skill, one that is different than traditional programming, that the author was building up over that period.

discipline is important. focus is hard. but allowing yourself to play is not a bad thing at all and i dont think building little interesting side projects should be a shameful act.

we are finding lots of value in self review. its the “imagine you are doing a synchronous paired review with someone - anything that is difficult to explain, has a code smell, doesnt fit the architecture of the system around you, write a comment.” then at the end, agents do a good job of looping over PR comments.

the second thing would be a guided, educational code review tool - there are a few attempts at this, but nothing that has a good enough interface to actually stick. organize hunks by semantic importance, spend some tokens exploring the surrounding systems, showing how new code, public apis and data model flow with the existing design, and allow a human to traverse larger PRs more quickly.

thank you to cloudflare for publishing this.

Occasionally I get this feeling for a large customer meeting or a public talk, because there are consequences and serious prep. But this is just trying to normalize extreme social anxiety and call it a management style.

One reason you get together to talk is so you can hash out details on potentially ambiguous topics, so you don't head in the wrong direction causing net negative contribution.

Another is that people are not automata. Humans require inspiration and motivation and you need to reinforce the vision of what you are building and why. Its also even sometimes a reasonable idea to ask about how their life is going and check up on their family and pets and career aspirations.

In general, some people should not be managers, and there is plenty of room in the world for super ICs.

Also, nice clever optimization here. Lots of low hanging fruit in harness land.

> We also plan to compile solved steps into micro‑policies. If you're running something like a RPA task or similar workflow as before, you can simply run the execution locally (with archon-mini running locally) and not have to worry about the planning. Over time, the planner is a background teacher, not a crutch.

Conceptually, I really like this - why re-do the work of reasoning about an already solved task? Just do it again. For some plausibly large majority of things, this could speed things up considerably.

> In the future we hope to run a streaming capture pipeline similar to Gemma 3. Consuming frames at 20–30 fps, emitting actions at 5–10 Hz, and verifying state on each commit.

I love targets like this. It makes you tune the architecture and abstractions to push the boundary of whats possible with a traditional agent loop.

The salience heat map compression is a great idea. I think you could take this a step further and tune a model so that it compresses an image into a textual semantic/interactive element hierarchy. This is effectively what browser-use is doing, just using javascript instead of a vision model.

This seems like a task that would benefit from narrow focus. I'm aware of the "Bitter Lesson," but my intuition seems to tell me that chaining together fit to purpose classification as an input to an intelligent planning system is the way to go.

I think it does boil down to "try things a lot," especially creating real connections with other people, even though you will painfully fail many times. Drive yourself to have real conversations. Protect your health and keep yourself strong physically and mentally. That's a powerful base to be standing on. Then go find a blend of interest, purpose and duty - building a sense of dharma helps you wake up in the morning and move through the world feeling a little less "lost."

Spatial reasoning and world model is one aspect. Posting bicycle part memes does not a bad model make. The reality is its cheaper than Sonnet and maybe around as good at Opus at a decent number of tasks.

> And, crucially, the failure to generalize adequately outside distribution tells us why all the dozens of shots on goal at building “GPT-5 level models” keep missing their target. It’s not an accident. That failing is principled.

This keeps happening recently. So many people want to take a biblically black and white take on whether LLMs can get to human level intelligence. See recent interview with Yann LeCun (Meta Chief AI Scientist): https://www.youtube.com/watch?v=4__gg83s_Do

Nobody has any fucking idea. It might be a hybrid or a different architecture than current transformers, but with the rate of progress just within this field, there is absolutely no way you can make a prediction that scaling laws won't just let LLMs outpace the negative hot takes.

Almost like hiring and scaling a team? There are also benchmarks that specifically measure this, and its in theory a very temporary problem (Aider Polyglot Benchmark is one such).