Alan Vazquez, CFA

Removing assumptions in order to improve my understanding of the real world and act upon evidence.
RT != Endorsement.

[Alan Vazquez, CFA @alanvaa96]

Definirse es limitarse.

[Khairallah AL-Awady @eng_khairallah1]

Google CEO, Sundar Pichai:

"If you don't learn to how to orchestrate agents now, you'll spend 2027 catching up to people who started today"

In 30 minutes he explains why the best engineers stopped writing code and started building agents.

Most people think building an agent requires an engineering degree.

It doesn't. It requires one guide and one afternoon.

Watch the interview, then save the exact setup below 👇

[darkzodchi @zodchiii]

Google CEO, Sundar Pichai:

"If you don't learn to how to orchestrate agents now, you'll spend 2027 catching up to people who started today"

In 30 minutes he explains why the best engineers stopped writing code and started running agents.

Watch the interview, then save the exact setup below 👇

[GREG ISENBERG @gregisenberg]

The takeaway from Fable 5 being BANNED by the government: GET GOOD AT LOCAL MODELS SO YOU HAVE 100% CONTROL.

My entire weekend was going to be building my craziest ideas with Fable 5. That's now cancelled.

So instead of building with Fable this weekend, I've decided I'll go deep on local models:

1. Start with the runtime. Download Ollama or LM Studio first. This is the thing that actually runs models on your machine.

2. Match the model to your hardware. A model's size is measured in billions of parameters (7B, 32B, 70B). Bigger is smarter but needs more memory. Rule of thumb: a 7B model runs on almost any laptop, a 32B needs a good Mac with 32GB+ RAM, a 70B needs serious hardware like a DGX Spark or a maxed-out Mac Studio.

3. Know which model for which job. Qwen 3 is the best all-around choice for most tasks. DeepSeek for reasoning and coding. Gemma 4 when you need something tiny that runs on a phone. Llama when you want the biggest community and the most fine-tunes.

4. Quantization. You can shrink a model to run on weaker hardware with barely any quality loss. Look for versions labeled Q4 or Q5. This is how a model that "needs" a server runs on your laptop. Learning this one concept changes everything.

5. Connect it to your agent. Point Hermes or your agent stack at a local model.

6. Context window is your real constraint locally. Cloud models give you huge context for free. Local models make you pay for it in memory. A bigger context window eats RAM fast. Keep your sessions tight and your prompts lean or your machine chokes.

7. Learn to give local models tools. A smaller local model with web search, file access, and code execution beats a giant model with none. The capability gap closes fast when you wire up the right tools. The model is the engine but the tools are the wheels.

8. Fine-tuning is more accessible than you think. You don't need this on day one, but know it exists. You can take an open model and train it on your own data so it gets good at your specific domain.

I'll probably do a breakdown at some point on this @startupideaspod if people are into it.

The lesson from this ban is basically don't build your entire workflow on something that can disappear with a single letter. Own part of your stack. Local models are insurance.

It reminds me when people realized they don't own social media accounts. And then you saw people build email lists etc.

I remember running a startup and my biggest traffic source was organic FB. All of a sudden, algo changed, and I lost 99% of my traffic.

Same sorta moment (but bigger) for AI.

This is a wake up call.

[Movez @0xMovez]

Claude Code creator:

"100% of our pull requests at Anrtopic are run by Claude Code. 80–90% of code review too.

The feature I’m using the most today is /loops. I’m not prompting Claude anymore - I’m building loops"

in 1-hour interview, Boris reveals his setup, which helps him build the #1 coding tool of this year.

Worth more than a $500 vibe-coding course.

[Satya Nadella @satyanadella]

https://t.co/vLmiBKTtX3

[David Sacks @DavidSacks]

I’ve had a number of conversations with folks inside and outside government about the current situation with Anthropic, and here is what I believe to be true:

— As we know, Anthropic publicly released its Mythos class models earlier this week under the commercial name Fable.

— Fable is Mythos with guardrails. But if those guardrails fail, then you’ve exposed Mythos and its advanced cyber capabilities to people who shouldn’t have them. (Keep in mind that Anthropic itself widely promoted the idea that Mythos was a cyberweapon and needed to be regulated as such. They asked for government regulation of Mythos and championed the guardrails on Fable. If there is a vulnerability — big or small — it is Anthropic’s responsibility to patch.)

— A highly credible trusted partner of both Anthropic and the USG who was testing Fable came forward with a jailbreak of those guardrails. The Admin asked Dario to fix the jailbreak or de-deploy the model. Dario refused.

— In their blog post, Anthropic defended its decision by saying the jailbreak isn’t serious. That is not what the trusted partner and the USG believe; nor is that kind of minimizing language consistent with Anthropic’s brand as the AI safety company. It’s difficult to fathom how they could claim a jailbreak allowing operability of a cyber weapon could be defined as not “serious.”

— In the past, Anthropic has always said that safety must be top priority and taken super seriously. In this case, Anthropic prioritized the continued offering of the consumer model over safety.

— In reaction, the Admin issued the export control. The Admin did this reluctantly. It’s been very surprised that Anthropic hasn’t wanted to cooperate with a reasonable safety request (ie fixing the jailbreak issue). Anthropic’s reaction is very much at odds with their branding and ethos as a safe AI research community.

— The Admin’s hope now is that Anthropic remediates the safety issue, the export control is lifted, and Fable goes back into general release. The Admin wants all of this to happen as soon as possible. It is frankly bewildered that Anthropic hasn’t wanted to comply with safety requests that it previously said were its highest priority.

— Those trying to misdirect and tie this action to the prior DoW/Anthropic issues are wrong. The Admin values Anthropic’s technical capabilities and feels that this issue, while serious, should be easily resolved. The ball is in Anthropic’s court.

[rody @0x_rody]

At Anthropic's event, Metaview engineer:

"We stopped fixing our prompts. The system reviews its own output and rewrites its own instructions now."

In 16 minutes, he shows the Claude Code loop running in production on thousands of reviews, not in a demo.

Watch the talk, then grab the full loop setup below👇

[Alan Vazquez, CFA @alanvaa96]

@benln come to Mexico!

[Rahul @sairahul1]

This is the best site on the internet to learn harness engineering.

Free. Completely.

Most AI engineers have never heard the term.

https://t.co/bwDbTTYsjM

Bookmark this site.

Then read this setup ↓

[Charly Wargnier @DataChaz]

🚨 ANTHROPIC JUST PUBLISHED A 36-PAGE SECURITY GUIDE THAT BASICALLY TELLS YOU TO STOP TRUSTING YOUR OWN AI AGENTS.

If you run agents on Claude Code, MCP servers, or automation tools, pay attention.

The attack timeline has collapsed.

AI models compress the gap between a vulnerability and a working exploit from months to hours, for mere dollars.

Agents introduce new autonomous risks, from tool poisoning to context memory manipulation.

The most useful idea in the guide is Anthropic's new security test:

Does a control make an attack impossible, or just tedious?

Automated attackers have unlimited patience. They will grind straight through friction like rate limits and 2FA. To defend at the speed of AI, you need hard barriers and automated defensive operations.

Here is how Anthropic says you should lock down agents:
→ Treat static API keys as compromised. Use short-lived tokens that expire in minutes.
→ Apply "Least Agency": explicitly limit what each tool can DO.
→ Sandbox agents that process untrusted inputs like emails and web pages.
→ Scope permissions dynamically per task, not permanently.

I've added the link to the guide in the 🧵↓

[Thariq @trq212]

been asking others at Anthropic how they stay in the loop with Claude and fully understand the work being done

this is one of my favorites from Suzanne:

[‏عادل | مبرمج @AdelDeveloperX]

🚨شوفت رئيس أكبر شركة ذكاء اصطناعي في العالم صدم الكل وقال إيه!

ديميس هاسابيس (رئيس Google DeepMind + حاصل على نوبل في الكيمياء) صدم الجميع في محاضرته بـ Cambridge وقالها علني:
"في المستقبل القريب، شخص واحد بيفهم الذكاء الاصطناعي هيتفوق على تيم كامل في شركة ناشئة"

احفظ البوست ده عندك دلوقتي عشان ترجع له قبل ما تنسى.

الموضوع بقى حرفياً مسألة وقت، والسر اللي الكبار بيخبوه طلع للعلن!

فيه جزء في المحاضرة دي مش قادر أوقف تفكير فيه:

- الـ AI اللي بتستخدمه النهاردة هو أغبى نسخة هتشوفها في حياتك، اللي جاي مرعب.
- كمان 5 سنين، الفجوة بين اللي بيستخدم الـ AI واللي مبيستخدموش هتبقى مستحيل تستخبى.
- الشركات هتدار بـ 10 أشخاص بس بيعملوا شغل كان بيعمله 200 موظف زمان.
- اللي هيوصلوا الأول مش الأذكياء، بل الناس اللي بدأت من دلوقتي صح.

حاليًا، الشخص الطبيعي بيفتح أي موديل، يكتب أي برومبت، ياخد الإجابة، ويقفل التاب.. هو فاكر إنه كده بيستخدم الذكاء الاصطناعي! بس الحقيقة هو مش مستغل أكتر من 10% من قوته.

بدل ما تضيع وقتك في سكرول ملوش لازمة، اتفرج على المحاضرة دي.. ده أوضح وأقوى شرح شوفته في حياتي من الراجل اللي خلّى الذكاء الاصطناعي يحل أعقد مشاكل البيولوجيا.

المحاضرة دي مفيدة جداً، سواء كنت مبتدئ أو بتستخدم الـ AI كل يوم.

نصيحة: احفظ الفيديو وشوفه حالاً عشان تشوف المستقبل رايح فين وتسبق الكل.

لايك وفولو واحفظ البوست عشان يوصلك كل جديد!

[Radha Tripathi @Radha_AI]

INSTEAD OF WATCHING AN HOUR OF NETFLIX TONIGHT.

This 60-minute Cambridge lecture by Demis Hassabis will teach you more about the future of AI than most people will learn in the next 5 years.

Bookmark it and give it an hour, no matter what.

[淘沙者(TheSandPicker) @Etudecn]

这段马斯克 2003 年在斯坦福的 45 分钟闭门演讲，真的有点东西。
不是那种空话连篇的创业鸡汤。
而是他亲自把“怎么从 0 搞出一家公司”一层层拆给你看。
更狠的是，
他没在讲概念，直接把自己当时手里那三家公司，到底怎么活下来的，整个过程都摊开了。
这种内容放到今天看都不过时。

很多人听完的反应都很统一：
这 45 分钟，顶得上你翻十本创业书。
那种真正能用的干货，往往就藏在这种老素材里，不吵不闹，但句句都是实战。

[Zephyr @Zephyr_hg]

https://t.co/1Qa2UvoPyx

[Movez @0xMovez]

Anthropic AI engineer just showed how to give AI agents real memory in 4 steps - and it changes everything

in 28 minutes he shows exactly how agents can remember across sessions, completely free

worth more than any $500 AI engineering course

here's what he covers:

• why agents forget everything between sessions
• memory stores - agents read, write across sessions
• dreaming - agents that improve their own memory
• 95% cache hit rate, so it stays cheap

most people are still copy-pasting context into every new chat - while the people who figured this out are building agents that get smarter every single night

watch full video then read article below

[zostaff @zostaff]

Jane Street, Goldman Sachs, JP Morgan, BlackRock, Hudson River Trading, Two Sigma, D.E. Shaw.

The most expensive engineering teams in the world released their financial tools on GitHub. Here are 7 repos, one from each.

1. Jane Street, janestreet/magic-trace
https://t.co/a2G20vnewK
5.3k stars. Process tracer powered by Intel PT. When your profiler is blind, magic-trace sees every CPU instruction.

2. Goldman Sachs, goldmansachs/gs-quant
https://t.co/SMYFwP3TWD
Derivative pricing the GS traders use at their desks. MIT licensed.

3. JP Morgan, finos/perspective
https://t.co/9rgy6FxYt4
What JPM traders use to watch markets in real time. A $24k/year terminal, for free.

4. BlackRock, blackrock/lcso
https://t.co/iHwsxZDZD9
Rust optimizer for portfolio problems. Where scipy gives up, this works.

5. Hudson River Trading, hudson-trading/corral
https://t.co/YhmrQFmYaZ
Structured concurrency for C++20. The foundation of HFT infrastructure at one of the largest U.S. trading firms.

6. Two Sigma, twosigma/flint https://t.co/ebEFqcDxJ6
Time-series joins on Apache Spark with temporal tolerance. Built for billions of ticks.

7. D.E. Shaw, deshaw/pyflyby https://t.co/uYDQKtnDVd
Auto-import for IPython and Jupyter. D.E. Shaw also funded the development of IPython itself.

Bookmarked it

[Alan Vazquez, CFA @alanvaa96]

@CepnikMaciej Not peso strenght but dollar weakness. MXN is thetering at the brisk of abysm if current administration fails (likely) to take actions against its narco goverment.

[Don Quijote de la IA @IA_Quijote]

🚨 Guarda esta MASTERCLASS

El profesor de Stanford Graham Weaver dio una conferencia sobre cómo DESTRUIR EL MIEDO y vivir una vida ambiciosa.

En el vídeo (32 minutos que he subtitulado al español) explica las claves para superar el miedo, dejar de jugar a la defensiva y diseñar un destino ambicioso.

4 LECCIONES sobre cómo construir una vida asimétrica:

1. Haz cosas difíciles (Do hard things)

El miedo suele disfrazarse de lógica para mantenernos en nuestra zona de confort.

Todo cambio positivo pasa por una etapa inicial donde las cosas empeoran antes de mejorar (worse first).

Para alcanzar lo que deseas, debes avanzar directamente hacia aquello que estás posponiendo o que te genera temor

2. Sigue tu propio camino (Do your thing)

Intentar cumplir los sueños de otra persona es una garantía de fracaso.

El sufrimiento es inevitable en cualquier trayecto de la vida; la clave radica en elegir un proyecto o causa por la que realmente valga la pena luchar.

El potencial de una persona se multiplica de forma drástica cuando opera bajo el motor del entusiasmo y la auténtica motivación.

3. Mantén el enfoque durante décadas (Do it for decades)

Los logros extraordinarios requieren tiempo y constancia a largo plazo.

El factor más determinante para el éxito es la constancia; casi ningún obstáculo puede resistir a una persona decidida que mantiene su energía enfocada durante 10 años o más.

4. Escribe tu propia historia (Write your story)

No permitas que la inercia o las expectativas externas dicten tu futuro.

Diseña de forma explícita el rumbo que deseas para tu vida, atrévete a pedir lo que quieres y toma acción inmediata para construir esa realidad

[Rony @Ronycoder]

INSTEAD OF WATCHING AN HOUR OF NETFLIX TONIGHT.

This 1 hour Stanford lecture by Joel Peterson will teach you more about negotiation and getting what you want than most people learn in years.

Bookmark it and give it an hour, no matter what.