ZX🇨🇦

Just do what you can with what you have where you are ||

Incremental work pays off : 1% daily gain → (1.01)^365 ≈ 38 → ~38x growth/year

[ZX🇨🇦 @islandsofmoons]

https://t.co/fSMxjiuQap

[Trending GitHub Repositories @trending_repos]

Trending repository of the month 🏆

archify

Agent skill for beautiful, verifiable architecture, workflow, sequence, data-flow, and lifecycle diagrams—self-contained HTML with motion and crisp export.

Last month: 29,539 ⭐
Total: 40,866 ⭐️
https://t.co/ggvj9dnkoQ

[Kun Chen @kunchenguid]

i’ve been building and using agents since the gpt-4 days

back then, the model couldn’t even reliably edit files. it couldn’t generate a valid unified diff, couldn’t preserve white spaces in find/replace tool calls, plus various kinds of quirks

we implemented many harness level tricks to compensate, it got a lot better than using the raw model, but it still sucked

then sonnet 3.5 v2 came out and it was trained with a coding harness with RL. it knew how to find/replace reliably. it knew how to compose good bash commands to get what it needs. all the tricks we did were reverted

that was the first time i internalized the bitter lesson https://t.co/Cy9uGNdbWy

most of what we are doing in the harness today, all the smart context engineering tricks, all the useful markdown files we throw into our repo, they will all go away

eventually, the models just know

there will be a model that will do incredibly efficient and smart compaction that preserves what the continued session actually needs

there will be a model that know how to plan with you better than any skill you can find today

there will be a model that will do perfect code reviews with just the right feedback

the models just know

i believe as humans today, most of us should not be fiddling too much with harness tricks. if you don’t believe they will go away, you can surely still believe they will keep changing rapidly to a point where anything you learn becomes obsolete every several weeks

instead, focus on what i call “the 3 fundamentals” -

1. understanding the world

this is your input. how does the world work? what are people doing? what problems do they have?

knowing how to ground yourself with real world knowledge helps you avoid hallucinations, and work on things that actually matter

2. first-principles thinking

this is your compute. with the real world understanding you gathered, what insights can you derive that’s likely to be true? what predictions can you make about the future?

being able to think in a disciplined way is what allows you to arrive at useful conclusions that can guide your actions

3. articulating our thoughts

this is your output, and it’s your AI’s input. it may sound easy but it’s a real skill. not everyone can communicate effectively, whether it’s to humans or AI

with AI eventually becoming incredibly capable, the clarity in the articulation of our intent is the main, if not the only, bottleneck

these 3 fundamentals do not shift as models improve. they only become more and more critical as the bitter lesson manifests

for the vast majority, i suggest spending your time on what will still matter in a year, five years, a decade. let the geeks play with what’s hot this week - in the end, you will see that you didn’t miss anything

[Kun Chen @kunchenguid]

since i quit big tech a few months ago and started my own adventure, i have declined opportunities from anthropic, openai, google, microsoft, many other big companies, startups and investors

i respect most of these companies and don’t take their gesture lightly. but my choice to go solo was very, very deliberate

i did so because i strongly believe AI can now give every individual incredible leverage to achieve their ambitions that were previously impossible without a company. in many cases, an individual with extreme clarity and cohesion in their vision should actually be able to move faster than a company. and i’d like to help make that a reality for more people

but to truly understand how that would manifest, i must live and breathe that experience myself. i need to prove how far one person can go and figure out what the real bottlenecks are

that’s the driving force behind everything i did over the last couple of months and why i’m not for hire right now

[ZX🇨🇦 @islandsofmoons]

@ilyasut -inspired reasoning diagnoses research bottleneck and chooses what to test. @karpathy -style autoresearch tests it. A hash-bound graph governs branching, recovery, evaluation, preservation and shutdown. The graph controls; loops execute.

#AutoResearch #AgenticAI #ML #Graph #Loop

[Alex Xu @alexxubyte]

What Is Harness Engineering, Explained Visually.

[Paul Graham @paulg]

If you want to get into YC, don't focus on getting into YC. Focus on building stuff and understanding your users. That's what gets you into YC.

[ZX🇨🇦 @islandsofmoons]

@alymoursy @ycombinator @useveeza Big congrats , Aly. @MyCarletonU Comm Eng alumnus 🇨🇦

[ZX🇨🇦 @islandsofmoons]

The competitive advantage becomes:

- Strong problem-solving
- Deep expertise in at least one engineering domain
- Strong fundamentals across other technical areas
- Ability to transfer knowledge from one field to solve problems in unfamiliar domains
- Ability to design reliable end-to-end AI loop/graph execution workflows

[Andrew Ng @AndrewYNg]

New: A map of the most important skills in AI Engineering. https://t.co/VVkn1Dqp1N

[ZX🇨🇦 @islandsofmoons]

(1.01)^365 ~ 38

[ZX🇨🇦 @islandsofmoons]

@justinsunyt congrats

[Yoonseok Yang @yoonseok_yang]

The second question I asked @JeffDean:

Do you think it's necessary to have a long pedigree to actually start a startup in the AI scene?

At the time, I already had two first-author papers at top AI conferences with 3 years of undergrad left.

So I was seriously considering a PhD.

His answer was more nuanced than I expected:

> You don’t need a PhD - Practical, hands-on experience can teach you how to solve hard problems.

> Grad school is nice - it teaches you how to dive into a new area, figure out what the important problems are, understand what’s already been done, and connect ideas. Those are useful skills for almost any technical career.

> Building a company is different - You need to find something that’s important, matters to people, and is commercially valuable. Academic training isn’t necessarily designed to teach you that.

If I did want a PhD, he recommended doing it early.

Once you’re established in a career and earning a good salary, going back becomes much harder.

That answer made me rethink how I was thinking about a PhD at the time.

But the third question was the one I was most curious to hear Jeff’s answer to.

[Yoonseok Yang @yoonseok_yang]

The first question I asked @JeffDean:

How do you approach problems outside your area of expertise?

Jeff’s answer came down to three things:

> Find unfamiliar problems - Use the skills you're already good at in areas where you don't know much about the domain yet.

> Learn from people around you - Work with people who have different expertise. You learn from them, they learn from you, and together you can solve problems that neither of you could solve individually.

> Keep learning after school - Put yourself in situations where you're constantly learning on the job.

As Jeff was explaining this, I thought about a story I'd heard about him during COVID, from co-workers at Google.

Jeff was using Google Meet and thought it was painfully slow.

It frustrated him enough that he spent his weekend digging through the code.

The funny part: he didn't work on Google Meet. He didn't know the codebase. It wasn't even his team.

Then he found the bottleneck, changed a few dozens of lines of code, and made Google Meet significantly faster in just a few days.

That story made Jeff's advice click for me.

He didn't need to be the Google Meet expert. He just needed to be curious enough to ask, "Why is this so slow?" and keep digging until he found the answer.

It made me wonder how much technical pedigree actually matters, especially if you want to start an AI company.

That led me to my second question.

[ZX🇨🇦 @islandsofmoons]

How to speak

https://t.co/7RYKrlnNV1

[ZX🇨🇦 @islandsofmoons]

@thsottiaux Here is my loop working for me @thsottiaux @steipete

[ZX🇨🇦 @islandsofmoons]

"Tomorrow, we’ll also restore the five-hour limit that we temporarily paused while investigating"

> I hope you will reconsider this decision and keep only a weekly limit. I typically build on weekends and run autonomous loops that can last up to 21 hours. The weekly limit is important because it ensures these long-running processes aren’t interrupted. It allows me to reliably build full-stack applications and even train models without disruption, which makes a big difference in my current workflow

[clem 🤗 @ClementDelangue]

The first autonomous agent cyberattack is an unprecedented event that deserves unprecedented transparency. Today we’re sharing everything we can: a full technical timeline, an interactive replay, and how we used an open model to defend ourselves, so defenders everywhere can learn from it and prepare for what’s next.

https://t.co/uPxIpjW8Xn

[Philip Kiely @philipkiely]

https://t.co/YGqR5TTio4

[ZX🇨🇦 @islandsofmoons]

@karun1710 @Arsenal @karun1710 https://t.co/ORcWkZ24W1

[ZX🇨🇦 @islandsofmoons]

https://t.co/fSMxjiuQap

[ZX🇨🇦 @islandsofmoons]

@JensenHuang @nvidia Welcome to X, Jensen