Filip Pyrek

AWS Hero 🚀 / Cloud Expert 🪄 / Engineering Innovator 🤖

[Filip Pyrek @FilipPyrek]

https://t.co/0SBbJubGua

[Filip Pyrek @FilipPyrek]

Ještě zbývá víc jak 20 dní do našeho prvního brněnského Agentic Engineering Meetupu ve Vertice a registrace už nám praskají ve švech 📈

Tak se zaregistruj dokud máme ještě místo! 📬

👉 https://t.co/0SBbJubGua

[Autodesk Fusion @adskFusion]

We have partnered with @AnthropicAI to launch the Claude connector for Fusion.

With the Fusion MCP, you can now tap into Claude’s desktop capabilities—speech, vision, web search, and cross-service workflows—right inside your design flow.

Combined with #AutodeskAssistant, this opens up more flexible, customizable agentic workflows for how you build with Fusion + Claude.

Learn more here: https://t.co/X2YCUlAnnz

[Filip Pyrek @FilipPyrek]

Somebody put my throughs into wiring. Check it out!

[Peter Pang @intuitiveml]

https://t.co/oWr2pTk4Hu

[Honza Pobořil @HonzaPoboril]

Skvělé tipy pro Claude Code (ale většinou aplikovatelné i na OpenCode a jiné)

Nechtěl se mi to číst, tak jsem si o tom nechat udělat podcast v češtině:
https://t.co/cU5ec3S6ey

[Google AI @GoogleAI]

Today, we’re evolving @StitchbyGoogle from @GoogleLabs into an AI design canvas transforms natural language prompts into production-ready front-end code.

Some highlights from what’s new:

1. A complete redesign of the Stitch UI, which can now ingest multimodal references (text prompts, images, or code) as creative seeds for your design ideas

2. A brand new, context-aware design agent that can share feedback on builds, generate PRDs, and ask questions to better understand your vision. You can even talk to the agent if you prefer a verbal sounding board

3. A new agent-friendly markdown file, DESIGN.md, which you can use to export or import your design rules to or from other design and coding tools

Whether you’ve been designing for decades or you’re whiteboarding your first software idea, Stitch can help you turn concepts into prototypes in minutes rather than days ➡️ https://t.co/efI6cbjAhm

[Filip Pyrek @FilipPyrek]

lol #ai #claude #claudecode #llm #aifirst

[Google Gemini @GeminiApp]

Today, we’re releasing a significant upgrade to our specialized reasoning mode, Gemini 3 Deep Think.

Deep Think is built to drive practical applications, enabling researchers to interpret complex data and engineers to model physical systems through code.

With the updated Deep Think, you can turn a sketch into a 3D-printable reality. Deep Think analyzes the drawing, builds the complex shape, and generates a file so you can create the physical object with 3D printing.

This is rolling out now to Google AI Ultra subscribers. Select the "Deep Think" option in the tools menu to get started.

Learn more here: https://t.co/MMGMgDtoK8

[Tomasz ��akomy @tlakomy]

Comparing Opus 4.6 to Opus 4.5

[Filip Pyrek @FilipPyrek]

End of an era.

[Heroku @heroku]

Heroku is transitioning to a sustaining engineering model focused on stability, security, reliability, and support. Heroku remains an actively supported, production-ready platform, with an emphasis on maintaining quality and operational excellence rather than introducing new features. We know changes like this can raise questions, and we want to be clear about what this means for customers.

There is no change for customers using Heroku today. Customers who pay via credit card in the Heroku dashboard—both existing and new—can continue to use Heroku with no changes to pricing, billing, service, or day-to-day usage. Core platform functionality, including applications, pipelines, teams, and add-ons, is unaffected, and customers can continue to rely on Heroku for their production, business-critical workloads.

Enterprise Account contracts will no longer be offered to new customers. Existing Enterprise subscriptions and support contracts will continue to be fully honored and may renew as usual.

Why this change

We’re focusing our product and engineering investments on areas where we can deliver the greatest long-term customer value, including helping organizations build and deploy enterprise-grade AI in a secure and trusted way.

[Darren Shepherd @ibuildthecloud]

I really think there's a huge advantage to vibe coding golang over typescript. Obviously, you wouldn't use golang for frontend. But if your in services land, forgo nodejs and do golang. It's a simple language that has a very consistent ecosystem and AI does well.

[Sandro Volpicella @sandro_vol]

I missed CDK Aspects when I moved to SST.
Being able to modify all resources at once is a superpower.
Turns out, you don't have to give that up.

You can auto-add alerts to every Lambda function in your stack with a simple transform.

Here is how to auto-monitor your entire stack:

[Filip Pyrek @FilipPyrek]

@tlakomy Eastern European classic 🤙🤙🤙

[Jay @jayair]

OpenCode

400K monthly active users
40K GitHub stars
400 contributors
2.5M downloads

It took us 5 months

But it’s based on everything we’ve learnt over 5 years

Building consumer apps, open source products, designing SDKs, TUIs, positioning, marketing

[Vidit Shah @Vidit_210]

If you're exploring DynamoDB and want a hands-on learning path, this resource can help a lot: https://t.co/BUXemc1Ngx

[Pentagon Pizza Watch @pizzintwatch]

POLYGLOBE JUST GOT ANOTHER MASSIVE UPGRADE 🌐

DeepStateLive’s Ukraine war map is now draped directly onto the globe, live and synced with @Polymarket .

Shaded control zones, shifting frontlines, Russian/Belarusian unit icons and direction of attack arrows all sitting under your markets and OSINT in near real time.

These Ukraine geo markets confuse people constantly. is it the city limits, the oblast, “enter” vs “capture all of”?

Now, when you hover a market, we draw the exact area of operation it resolves on and spell out the rule in plain English.

No more rule debates, no more guessing what actually settles.

The only way to monitor the situation.

[Sandro Volpicella @sandro_vol]

AWS finally made centralized logging simple 🎉
Everybody says "you need a central logging account" but creating it was quite a hassle.

Your options before:

🔹 Observability Access Manager (OAM)
Works, but complicated setup.
Requires understanding sinks, sources, and links.
Not exactly plug-and-play.

🔹 Custom Solutions
Log Subscription filters + Kinesis Data Streams + Lambda.
You're now maintaining infrastructure instead of building features.

🔹 Third-Party Tools
Works great!
Costs more.
Another vendor dependency.

AWS just launched CloudWatch log centralization.
Built into Organizations 🔥

What it does:
Creates rules that automatically copy logs from source accounts to your central logging account.

Cross-account.
Cross-region.
No Kinesis needed.

Setup is three steps:

1️⃣ Delegate your logging account as administrator
2️⃣ Create centralization rules (by account ID, OU, or entire org)
3️⃣ Logs start copying over

You can create up to 50 rules.
Logs include @aws.account and @aws.region fields for filtering.

If you run multiple AWS accounts, this is the simplest path to centralized observability.

---

Hey, I’m Sandro — a full-stack engineer who’s built dozens of production-grade apps on AWS. I share what I learn with 11,000+ devs at https://t.co/tNh21GIyjQ.

[Charly Wargnier ♨️ @DataChaz]

If you’re still sending raw JSON into your LLMs, you’re burning tokens, latency, and budget!

Try TOON (Token-Oriented Object Notation).

Clear like YAML, compact like CSV:

• 30–60% fewer tokens
• Up to 50% lower costs
• Shines for tabular data.

Free and Open source 🧵↓

[Filip Pyrek @FilipPyrek]

This is a big one. As your data teams might have noticed - Jupyter's team is crumbling and it's sad to watch after all the honorable work they have done. That's why we decided to get on a mission at Deepnote to build an open-source successor to #Jupyter. 🚀

Try it out and let me know what do you think 👀👂

[Deepnote @DeepnoteHQ]

We've spent 7 years building the data notebook for AI era. Today, we're open sourcing it.

Deepnote Open Source is successor to the Jupyter notebook. It acts as a drop-in replacement for Jupyter with an AI-first design, sleek UI, new blocks, and native data integrations. Use Python, R, and SQL locally in your favorite IDE, then scale to Deepnote cloud for real-time collaboration, Deepnote agent, and deployable data apps.

Single-player notebooks were great in 2013.

2025 needs reactive, collaborative, AI-ready projects that integrate into your existing stack seamlessly.

That's why we're making Deepnote open source - to offer the community an open standard for AI-native data notebooks and data apps.

We're standing on the shoulders of Jupyter — it changed how the world explores data. But at team scale, the papercuts stack up: brittle reproducibility, no native data connectors, weak collaboration, and bolted-on AI features.

In the enterprise context, this gets very tough to manage - and we're seeing an increasing demand from large companies to move away from Jupyter.

What’s new:

- Reactive execution (downstream block auto-update)
- Powerful blocks beyond code: SQL, interactive inputs, charts, KPIs, buttons
- 100+ data integrations
- Code in your favorite IDE: Cursor, Windsurf, or VS Code
- No lock‑in: open standard; export to `.ipynb` whenever you need

Once you're ready to scale in your team, transfer to Deepnote Cloud with one command for beefier compute, powerful data apps from notebooks and agentic data science.

Ty it now:

Repo ➔ https://t.co/sLam1F9CDk
Deepnote in VS Code ➔https://t.co/3hil38W0QO
Docs -> https://t.co/D81lu1WTdH
CLI ➔ `npx @deepnote/convert notebook.ipynb`

P.S. This only works as an open standard. Tell us what’s missing, file issues, send PRs.
Help define the data notebook for the AI era!

[Akshay 🚀 @akshay_pachaar]

Microsoft did it again!

Building with AI agents almost never works on the first try.

You spend days tweaking prompts, adding examples, hoping it gets better. Nothing systematic, just guesswork.

This is exactly what Microsoft's Agent Lightning solves.

It's an open-source framework that trains ANY AI agent with reinforcement learning. Works with LangChain, AutoGen, CrewAI, OpenAI SDK, or plain Python.

Here's how it works:

> Your agent runs normally with whatever framework you're using. Just add a lightweight agl.emit() helper or let the tracer auto-collect everything.

> Agent Lightning captures every prompt, tool call, and reward. Stores them as structured events.

> You pick an algorithm (RL, prompt optimization, fine-tuning). It reads the events, learns patterns, and generates improved prompts or policy weights.

> The Trainer pushes updates back to your agent. Your agent gets better without you rewriting anything.

The best part: you can also optimize individual agents in a multi-agent system.

I have shared the link to the GitHub repo in the replies!

Let me know if I should cover this in a video demo!