Felipe Sanchez

Engineer, interested in #AI #DataSciences to #SupplyChain and

[Felipe Sanchez @fesan81]

Which is the best #MachineLearning technique to #Forecast #DataSeries ?
I wrote a benchmark in this post @Medium : https://t.co/Dt1xIsO8rR #DataScience #ArtificialIntelligence

[Google Research @GoogleResearch]

Introducing TabFM, a foundation model designed specifically for tabular data classification & regression. This approach allows generation of high-quality predictions on previously unseen tables in a single forward pass.

Learn more and try out the model →https://t.co/OTbVQ8oUQs

[Andrew Ng @AndrewYNg]

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build.

Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention.

The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention!

Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on.

The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience.

When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful.

AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system.

External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent.

With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both!

I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering).

[Original text: The Batch]

[Hasan Toor @hasantoxr]

🚨 Google DeepMind + Meta + Amazon just dropped a 100 page roadmap that redefines what AI agents actually are.

They analyzed every major agentic reasoning framework across planning, tool use, memory, and multi-agent coordination.

Single-agent in stable environments: works great.
Multi-agent in dynamic environments: falls apart.

The problem isn't intelligence. It's how agents reason under uncertainty.

Three layers they found that actually matter:

→ Foundational reasoning (planning + tool use in stable settings)
→ Self-evolving reasoning (agents that adapt through feedback and memory)
→ Collective reasoning (multiple agents coordinating toward shared goals)

Even the best models struggle in open-ended environments.
Closed-world benchmarks are lying to you.

Reinforcement learning helps post-training. But in-context reasoning at test time? Still broken for long-horizon tasks.

The real finding: most AI agent tutorials you've seen are built on single-task, static benchmarks.

Real-world deployment robotics, healthcare, autonomous research breaks every framework on the market.

And almost nobody is building for that gap.

The future isn't smarter models. It's agents that plan, act, and learn across dynamic environments without falling apart mid-task.

This survey is the closest thing to a unified blueprint for that.

100% free on arXiv.

[Felipe Sanchez @fesan81]

@platzi @n8n_io Muy buen curso súper facil de entender y aplicar . @platzi gracias! @n8n_io
Mi certificado:

[Ihor Stefurak @ihorstefurak]

✨ I spent the last 10 hours building a chrome extension with ChatGPT as my CTO.

We made an invisible AI assistant that lives in the text boxes of websites you visit. One command, infinite possibilities.

Just type /ai + add your prompt -> https://t.co/QLBH6NBHaP

[Levi @levikul09]

The math behind Bayes' Theorem clearly explained!

🧵

[Judea Pearl @yudapearl]

Glad to be witnessing the democratization of causality. Wikipedia has spawned a new entry on "Causal Model" which is a concise summary of #Bookofwhy, almost in its entirety:
https://t.co/yXxo4WbWiS
Barring a few glitches, I am surprised it came out faithful and punchy.

[PMSTAC @pmstac]

How to make Project Management Maturity Models useful for Agile Projects? by @fesan81
#PMOT #Agile #PM https://t.co/zOYExKEOcn

[Dr. Theophano Mitsa ☦️🇬🇷🇺🇸 @theomitsa]

#MachineLearning
How to build a complex Hybrid AI model to predict oil and gas project over costs ? by @fesan81 https://t.co/p1lfDAW3fe

[Felipe Sanchez @fesan81]

@prestodb @linuxfoundation I think this event is a great initiative. I invite you all to join !!

[Presto @prestodb]

The initial schedule for #PrestoCon 2021 has been released! 🎉 Don't miss the largest #PrestoDB event of the year - registration is free. Check out the awesome talks scheduled and sign up today! 👉
https://t.co/LIZlzFJYxy h/t @linuxfoundation #opensource #Presto #SQL

[Felipe Sanchez @fesan81]

Given the energy crisis, can we use datascience to predict overcost in energy proyects?
#data #datascience #Oil #energy #energy_cost #prediction #gas #SupplyChainCrisis #machinelearning
@TDataScience https://t.co/jqWW7zQtfb

[Felipe Sanchez @fesan81]

@TDataScience @__jakegrigsby__ Great explanation ! Thanks for sharing

[Towards Data Science @TDataScience]

Multivariate Time Series Forecasting with Transformers by @__jakegrigsby__ https://t.co/mLGv24CuJP

[Felipe Sanchez @fesan81]

One of the basic #Algorithm of supervised #machine_learning is #knn.
This post explain easily how to implement this algorithm in #Python
https://t.co/xkjF1QQaFU

[Daniela Sánchez @Daniela21920604]

La #creatividad es un pilar de mi vida :) si quieres conocer mas de mi trabajo te invito a visitar mi #Paginaweb https://t.co/3l3v9DAahT

#fotografia #arte

[Felipe Sanchez @fesan81]

@magarzonr Como volver a usar twitter

[Felipe Sanchez @fesan81]

@magarzonr Mejor ;)