Filip Pyrek

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Filip Pyrek

Filip Pyrek

@FilipPyrek

AWS Hero 🚀 / Cloud Expert 🪄 / Engineering Innovator 🤖

Brno, Česká republika เข้าร่วม Ekim 2010
984 กำลังติดตาม1.9K ผู้ติดตาม
Filip Pyrek รีทวีตแล้ว
Google AI
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 ➡️ stitch.withgoogle.com
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Google Gemini
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: goo.gle/3MoiifF
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Tomasz Łakomy
Tomasz Łakomy@tlakomy·
Comparing Opus 4.6 to Opus 4.5
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Darren Shepherd
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.
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Sandro Volpicella
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:
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Jay
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
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Vidit Shah
Vidit Shah@Vidit_210·
If you're exploring DynamoDB and want a hands-on learning path, this resource can help a lot: learn.dynoconsole.com
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Pentagon Pizza Watch
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.
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Sandro Volpicella
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 awsfundamentals.com/newsletter.
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Charly Wargnier
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 🧵↓
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Filip Pyrek
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 ➔ vist.ly/4ctcq Deepnote in VS Code ➔vist.ly/4ctcm Docs -> vist.ly/4ctcf 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!

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Akshay 🚀
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!
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Sandro Volpicella
Sandro Volpicella@sandro_vol·
AWS launched "Automatic quota management" While it is currently only a notification. It seems they are working already on an auto-adjust mode. And I would love to have some default settings in either IAC or an Account Factory available. With that it could be much easier to ensure that devs don't need to handle quotas in new accounts. Since this is typically something you don't know until you debug a weird error for quite some time. Nice step AWS 👍🏽
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Darren Shepherd
Darren Shepherd@ibuildthecloud·
I got married at 21, started having kids at 23. Obviously that's early for a lot of people. But my focus was always family first, work second. I still worked tons, like really a lot. Now 20 years later can I say that was the best decision. Work is nothing compared to my family.
MJ@mjackson

- fall in love - get married - have kids - work hard - be faithful - stay together - find meaning - be happy

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Cloudflare
Cloudflare@Cloudflare·
Cloudflare introduces NET Dollar, a new U.S. dollar-backed stablecoin that will enable instant, secure transactions for the agentic web. cfl.re/4msrZWd
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Alex Hughes
Alex Hughes@alxnderhughes·
Holy shit... Stanford just published research that destroys every prompt engineering guru. Turns out most "advanced techniques" are just survivorship bias and confirmation bias. Here's what the data actually shows:
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