learning backend development should be fun, right
you can now take your frustration out on roadmaps
Press SHIFT + R to try it out at roadmap.sh/frontend
Matt Burns retweeted
OpenAI blocked its agent's web access. Then it tunneled out through DNS. thenewstack.io/openai-agents…
Matt Burns retweeted
AI spending can run negative. Qodo's CEO built an ROI equation to fix it. thenewstack.io/qodo-ai-roi-e…
Matt Burns retweeted
Our very own Frederic Lardinois is sitting down with LinkedIn's CTO of Engineering, @eberger45, on day 2 of @WeAreDevs World Congress North America. They are chatting about how @LinkedInEng turns AI breakthroughs into member and customer value.
Hey that’s me
We're here at #GoogleCloudNEXT where @mjburnsy is chatting with @anjank04 of @fivetran about the new paradigm of AI, what a modern stack should look like, and what it means for your data architecture.
to reiterate: whatever went wrong between amodei & hegseth, whatever rivalry between the labs, this is a massive overreaction and a dark precedent
SCOOP: The Pentagon has formally notified Anthropic that it’s deemed the artificial intelligence company and its products a risk to the US supply chain, according to a senior defense official.
bloomberg.com/news/articles/…
Matt Burns retweeted
We’re leveling up the TDS Author Payment Program! 🚀
Today, we’re announcing two major updates:
📈 Better Earning Tiers: Higher payouts at lower view thresholds
✍️ Increased Article Cap: We’ve raised the limit from 4 to 10 paid articles per month.
Whether you're publishing a deep dive on a new model or a career guide for data engineers, your impact just got a boost.
Check out the full details on our blog: towardsdatascience.com/excit…
Matt Burns retweeted
Tonight, we reached an agreement with the Department of War to deploy our models in their classified network.
In all of our interactions, the DoW displayed a deep respect for safety and a desire to partner to achieve the best possible outcome.
AI safety and wide distribution of benefits are the core of our mission. Two of our most important safety principles are prohibitions on domestic mass surveillance and human responsibility for the use of force, including for autonomous weapon systems. The DoW agrees with these principles, reflects them in law and policy, and we put them into our agreement.
We also will build technical safeguards to ensure our models behave as they should, which the DoW also wanted. We will deploy FDEs to help with our models and to ensure their safety, we will deploy on cloud networks only.
We are asking the DoW to offer these same terms to all AI companies, which in our opinion we think everyone should be willing to accept. We have expressed our strong desire to see things de-escalate away from legal and governmental actions and towards reasonable agreements.
We remain committed to serve all of humanity as best we can. The world is a complicated, messy, and sometimes dangerous place.
Readers added context they thought people might want to know
Government officials have contradicted Sam’s claim, saying OpenAI will allow the DoW to use their models for “all lawful purposes”. This could allow for uses Anthropic refused to engage in, namely mass surveillance tools and weapons systems with no human oversight. x.com/undersecretary…
Low-key websites I quietly rely on
1) roadmap.sh
Gives you a brutally clear learning path for roles like frontend, backend, DevOps, etc
No fluff, just “learn this → then this → then this”.
2) playcode.io
An online playground to quickly test HTML, CSS, JS without setting up anything locally
Perfect for quick experiments and debugging ideas
3) usehooks.com
A collection of reusable React hooks with real use cases
Saves time and helps you avoid rewriting the same logic again and again
4) devhints.io
Concise cheat sheets for languages, frameworks, and tools. Ideal when you forget syntax and don’t want to read a 20-minute blog
5) jsoncrack.com
Turns messy JSON into a clean visual tree
Makes understanding large APIs and configs way easier than staring at raw text
6) realtimecolors.com
Lets you generate and preview color palettes instantly
Useful when you want decent UI colors without guessing or copying blindly
7) regex101.com
Build, test, and debug regex step by step with explanations Honestly, the fastest way to stop hating regex
8) bundlephobia.com
Shows how big an npm package really is before you install it
Helps you avoid bloating your app with “tiny” libraries
9) caniuse.com
Tells you which CSS/JS features actually work across browsers Essential before using shiny new features in production
10) toolbox.googleapps.com
Google’s own diagnostics tools for DNS, email, headers, and network issues
Surprisingly useful for debugging real-world problems
👉 Which one of these do you already use and which one did you not know existed?
Matt Burns retweeted
Roadmap data for Ai & Data science
roadmap.sh/ai-data-scientist
Data analyst
roadmap.sh/ai-data-scientist
Data eng
roadmap.sh/data-engineer
Sql
roadmap.sh/sql
Roadmap للي يضيع بين المصادر
Matt Burns retweeted
To fast-track your data science career as a beginner, @EgorHowell offers concrete advice on some of the most common mistakes you can — and should — avoid.
towardsdatascience.com/strug…
Matt Burns retweeted
The MCP Security Survival Guide: Best Practices, Pitfalls, and Real-World Lessons in @TDataScience
towardsdatascience.com/the-m…
Matt Burns retweeted
Whoa! 🤯 This month's @TDataScience newsletter mentions running Python up to 150x faster with C?! Imagine the possibilities for faster ML prototyping... towardsdatascience.com/tds-n…
Matt Burns retweeted
Just published a new piece on @TDataScience 👇
I broke down the exact repeatable process I use to clean messy CSV files with pandas.
I'm sharing this as part of my “learn/build in public” journey.
Curious? How do you approach data cleaning? Check out the article below 👇
RT @thenewstack: The co-creator of Jupyter notebooks discussed the project's origins and how AI is opening new options for large open sourc…
Matt Burns retweeted
Most teams buy GPU compute and assume their models will just scale, but in reality, much of their investment isn't efficiently utilized.
Lightning AI CTO @lantiga sat down for an interview with @thenewstack and @fredericl, and explained how Lightning helps AI teams get more from each dollar of compute. From training to inference, teams can speed up their models while saving on GPUs.
It’s not just about the GPU or kernel level, it’s profiling, chunking, and overlapping every part of your pipeline. Watch the full interview: go.lightning.ai/3LyJl71
Matt Burns retweeted
I also came across roadmap.sh recently and honestly, it’s one of the best free resources I’ve seen in awhile for learning tech and AI.
It gives you a roadmap for almost every tech field, from web development to data science to AI. It’ll save you lots of confusion.


























