Andres Chacon

Next Big Thing Seriously.

[Andres Chacon @andresch79]

@OtroFutboleroHn Porque dejan vivir

[Andres Chacon @andresch79]

@thsottiaux My Tour-guide CHatbot !!!

[Andres Chacon @andresch79]

@UlisesDavid__ pero no hace nada garna, que querian que hiciera . Tomatelaa

[Andres Chacon @andresch79]

@puravidasports @bryanruizcr @FFullham @premierleague

[Anatoli Kopadze @AnatoliKopadze]

Anthropic engineer:

"Fable 5 is already smarter than we know how to use. The bottleneck was never the AI, it's you."

In 19 minutes he shows exactly how to get everything out of Claude with no extra tools, no extra costs.

You're already paying for all of this, just not using it.

Watch the session, then read the guide below on the Claude features 99% of users never find.

[Andres Chacon @andresch79]

@UlisesDavid__ Capo dibu

[Andres Chacon @andresch79]

@AlfieJCarter Stack

[Andres Chacon @andresch79]

@ky_markets GUIDE

[Shann³ @shannholmberg]

how to set up your Hermes Agent control room

send this image + the repo below to your agent and it will configure itself based on the blueprint

https://t.co/Bb2nEm96i8

this is the same architecture I use to run specialist agents across both my agencies

[Nico @nicos_ai]

GITHUB ACABA DE LANZAR LA CERTIFICACIÓN OFICIAL DE UNO DE LOS ROLES TECH MÁS IMPORTANTES DE 2026

→ Agentic AI Developer (GH-600)

Y es la primera vez que trabajar con agentes de IA se convierte oficialmente en una disciplina reconocida de ingeniería.

Ya no hablamos de:
• prompt engineering
• vibe coding
• automatizaciones simples

Hablamos de un nuevo perfil técnico:

→ Agentic AI Developer

La persona que:
• coordina agentes de IA
• construye workflows autónomos
• integra agentes en entornos reales
• supervisa fallos en producción
• evita errores críticos en pipelines CI/CD
• sabe cuándo un agente no es fiable

Antes:
→ “Trabajo con agentes de IA” era difícil de validar.

Ahora:
→ GitHub certifica oficialmente ese skillset.

Y eso cambia el mercado.

Las empresas van a necesitar este perfil.
Pero todavía hay muy pocos developers especializados en ello.

Si ya trabajas con:
• Copilot
• Codex
• Claude Code
• workflows agentic
• automatizaciones con IA

Probablemente ya estés haciendo este trabajo.

GH-600 es la forma de demostrarlo.

Guárdate esto 🔖

[Andres Chacon @andresch79]

@MikeZillionaire Ai

[Nav Toor @heynavtoor]

Here are 10 GitHub repos that quietly print money while you sleep.

1. Cal. com
Open-source Calendly. Fork it, white-label it, sell to dentists and lawyers for $200/month. The founders hit $5M ARR in 3 years doing exactly this.
Repo → https://t.co/haz8ihRsHm

2. Plausible Analytics
Privacy-first Google Analytics. Self-host it, resell to agencies for $50/month per client. Two founders bootstrapped this to 7 figures.
Repo → https://t.co/RFrcpqTBQ7

3. Ghost
Open-source Substack with 100% margin. 1,000 readers at $5/month equals $60,000 a year. Forever.
Repo → https://t.co/Z1MdZ5Zapg

4. n8n
Open-source Zapier. Sell automation services for $500-$2,000 per setup. n8n raised $14M because the agency model behind it works.
Repo → https://t.co/hdycABGGc1

5. Supabase
Free Firebase replacement. Build a SaaS in a weekend, charge $29-$99/month. They raised $116M for a reason.
Repo → https://t.co/dFB2QvafA7

6. Medusa
Open-source Shopify. Take 5% on every sale forever. Zero rev share to Shopify.
Repo → https://t.co/uEuCK6zuZO

7. AppFlowy
Open-source Notion. Sell self-hosted to enterprises worried about data privacy. They raised $30M because this market is massive.
Repo → https://t.co/IDMykTCkMU

8. Coolify
Open-source Vercel and Heroku. Charge developers $20/month to manage their deployments. Replace their $200 Vercel bill.
Repo → https://t.co/N5Fk22qraT

9. Listmonk
Open-source Mailchimp. Send unlimited emails for the cost of an AWS bill. Resell to agencies at 10x markup.
Repo → https://t.co/NS6Uukcklw

10. Penpot
Open-source Figma. Sell self-hosted design tools to agencies who refuse to upload client files to the cloud.
Repo → https://t.co/Lx1CYUP4p4

The difference between developers who build features and developers who build businesses is one decision.

Pick one of these. Fork it this weekend. Ship it next week.

The founders behind these repos already proved the model.

Save this. Share it with the developer in your life who deserves to break free.

100% free. 100% open source.

[Andres Chacon @andresch79]

I just won on @Polymarket. https://t.co/iVzWmfcWJ4

[Andres Chacon @andresch79]

I just won on @Polymarket. https://t.co/ctIcYctvQZ

[Andres Chacon @andresch79]

@marryevan999 Claude

[Andres Chacon @andresch79]

@marryevan999 Openclaw

[Marry Evan @marryevan999]

I was woken up at 3:47 AM by OpenClaw

It sent just one message:
"Found 6 markets that will settle in the next 90 minutes. Americans are still asleep. Need approval to deploy $12K."
I replied with a yes and went back to sleep

Woke up in the morning, and my account had gained:
+$43,800
I've been running this agent for 9 days
It does one thing specifically:
Watch for timezone arbitrage

I fed OpenClaw a few types of real-time feeds from different time zones:
Japan government RSS
European Parliament schedule
Australian financial alerts
Middle East flight tracking
Asian central bank announcements

Then I gave it just one rule:
"Find markets that settle between 2 AM and 6 AM Eastern Time. If the edge exceeds 30%, wake me up."
And at 3:47 AM, it actually found 6 markets
All settling between 4 AM - 6 AM

These markets had one thing in common:
The market was still pricing on a "normal rhythm"
But when settlement happened, US traders were basically all asleep
The official signals from the relevant countries had actually come out early

The alerts it pushed to me at the time were:
"Japan rate decision - BOJ leak shows YES 68%, Polymarket still at 23¢"

"EU emergency vote - Live footage shows YES already leading, Polymarket still at 31¢"

"South Korea policy - Government RSS has confirmed, Polymarket still at 19¢"
"Australia trade deal - Minister stated publicly 2 hours ago, Polymarket still at 27¢"
"UAE production cut - OPEC meeting minutes already public, Polymarket still at 15¢"

"Singapore regulation - Parliament session still live-streaming, Polymarket still at 22¢"

Its summary was pretty straightforward:
Potential edge: $43K
Window: 90 minutes
Required capital: $12,000
I was half-asleep at the time, phone buzzed once

Opened Telegram and saw just one line:
"approve or miss"
I replied yes, then went back to sleep
By 7:30 AM when I woke up, all the notifications had come in

All 6 markets settled during morning hours in Asia / Europe
While US traders were waking up, the markets were already done

My entry prices were roughly:
15¢ - 31¢

Final settlements all hit:
95¢ - 100¢

Profit breakdown:
Japan: $8,200
EU: $6,900
Korea: $11,400
Australia: $7,100
UAE: $5,800
Singapore: $4,400

Total:
+$43,800

Later when I checked the logs, I realized this agent had been monitoring these markets for 8 to 14 hours
Constantly syncing official sources
Constantly waiting for US traders to go to sleep

Then it only struck in that instant:
Results overseas were basically confirmed
Prices on the US side hadn't updated yet
And settlement was already close

This edge boils down to something pretty simple:
Polymarket is 70% US traders
But events around the world never happen on EST time

While you're sleeping, the markets keep settling
This play of specifically exploiting info gaps during "when Americans are asleep" hours—do you think it's timezone arbitrage, or is it edging into the most basic form of insider advantage?

Giving This Free for 24 hours. To get it:

1. Comment the word 'Openclaw'
2. Like and Retweet this post
3. Follow me @marryevan999 (so i can DM you)

[Avi Chawla @_avichawla]

The more your agent remembers, the less it knows.

This sounds counterintuitive, but it is actually a direct result of how agent memory is built today.

Agent memory inherits the cognitive shape of its store.

- A vector DB gives it associative memory to recognize familiar patterns.
- A graph gives it relational memory to understand how things connect.

Most agents run on the first and skip the second.

Here's an example that explains the failure it leads to:

Say a study assistant stores three facts about a student in a vector DB:

- Mark is in grade 10.
- Grade 10 has final exams in March.
- The library closes 2 weeks before final exams.

Mark asks: "Will the library be open next week?"

The vector DB likely returns the first and third facts, because the query mentions Mark and the library.

But it skips the middle fact, which links Mark's grade to the exam time, because that fact mentions neither Mark nor the library.

It sits in embedding space too far from the query to make it to the retrieved context.

So the Agent answers with partial info, or it fills the gap with a plausible guess that sounds right but might be off by weeks.

This is not a corner case, but it's actually what real queries look like. Any question that spans two or more hops exceeds what a similarity search can do.

Increasing context size and retrieving more context is one solution.

But accuracy drops over 30% when the relevant fact sits in the middle of a long context, which is the well-known "lost in the middle" problem.

A bigger window is not the same as better memory. It just gives the model more room to miss things.

To actually solve this problem, you need to stop treating memory as a single store and start treating it as three complementary layers, each doing a job the others cannot.

- Relational: It stores where a fact came from, when it was stored, and who has access. This is the provenance layer.

- Vector: It stores what a fact means and what it is semantically similar to. This is the retrieval layer.

- Graph: It stores how facts connect, what depends on what, and who relates to whom. This is the reasoning layer.

All three are important and complementary:
- A vector DB alone gives similarity without relationships.
- A graph alone gives relationships without semantic search.
- A relational store alone tracks where data came from but cannot reason over it.

If you want to see this in practice, Cognee (open-source) implements this approach.

It runs an ECL pipeline (Extract, Cognify, Load) that writes into all three stores in a single pass and keeps them synchronized as new data arrives.

So the vectors and graph edges are built together during indexing, not glued together later.

On top of this, there are two things Cognee does differently from most memory tools:

1) Smarter entity resolution:

You can give Cognee a domain vocabulary file, and it uses it to merge duplicate mentions automatically.

So "car manufacturer," "automobile maker," and "vehicle producer" collapse into one canonical node instead of being available as three separate entries.

2) Local-first defaults:

The default stack runs on a single pip install and stays fully local. You can switch to Postgres and Neo4j for production without changing the API.

My co-founder wrote a first-principles walkthrough of agent memory that takes the same problem and works through every layer of the stack, ending in a real working agent built on Cognee.

Read it below.

[Evan Luthra @EvanLuthra]

The Head of Claude Code at Anthropic said he hasn’t written code by hand in months.

In 2 days he shipped 49 full features. All written 100% by AI.

He just dropped a 30 min talk on exactly how he does it.

Worth more than any $500 vibe coding course. Bookmark it: