Tisan Biliyok 侘寂

LOOOL Love the celebration from this kid to the Gyokeres goal 🤣🤣

🚨 A junior at Jane Street reportedly landed a $220K–$600K role because he used AI to analyze trillions of data points faster than most teams ever could. In this 1-hour lecture, he breaks down the exact system behind it: • how he researches massive datasets • how AI finds patterns humans miss • how his machine turns raw data into decisions • how you can apply the same thinking yourself Skip Netflix tonight. Watch this instead. One hour could completely change how you think about research, AI, and opportunity.

@premierleague @Arsenal Mtscheww!

Viktor Gyokeres wasn't giving up 😤 @Arsenal

This is the rest of the clip. Have a look at Bruno reaction to the bench near the end "Three times??!!" These players have amatuers on the bench to work with Good preparation (arguably) is one thing but top coaches/managers have solid in game management Ours has none. #nufc

@HappyPunch @L_aidback

Zaragoza goalkeeper just KO’d another player with a haymaker HOLY SH*T 😭

Genuinely don’t think we have had a worse striker in the history of the club. Sanogo etc were all better than this.

This is bad!

Advanced SQL topics that will take your data engineering game to the next level: - GROUPING SETS, ROLLUP, CUBE These functions allow you to do multiple aggregations at the same time without having to use UNION. They’re more performant that doing the aggregations individually and using UNION. - CROSS JOIN UNNEST / LATERAL VIEW EXPLODE These functions allow you to turn array columns into rows. This is very powerful when dealing with complex data! - REDUCE / TRANSFORM These functions are used to array data types and allow you to do array aggregations and element-wise operations. Very useful as well!

Anthropic pays engineers $750,000+ a year to understand how LLMs work. Stanford just put a 2 hour lecture that covers 80% of it for FREE. Bookmark this. Give it 2 hours today. It might be the highest ROI thing you do this month:

In 14 minutes, this Anthropic engineer who wrote "Building Effective Agents" will teach you more about building them right than most developers figure out on their own in months. Bookmark this for the weekend. Then read the builder's guide below.

We’re announcing a $60M extension to our Series D from @AMD, @Arm and @QualcommVenture, bringing together partners across the automotive compute stack to simplify integration and accelerate time-to-market of our AI Driver across ADAS and automated driving systems worldwide. wayve.ai/press/series-d…

Uber commits $10bn to robotaxis in strategy shift ft.trib.al/7Ri6156

Hi friends, I am applying for the Mustapha Abdullahi Energy Leadership Fellowship, an APC National Youth Wing initiative. To avoid being disqualified, I must get at least 10 likes and repost on my pitch video. Pls, help me reach my target, thank you 🙏🏽

Data engineering will start feeling like Microsoft excel soon enough! - data pipelines generated with Claude Code: companies threatened: Informatica, Talend, Fivetran - physical data modeling managed by Databricks liquid clustering Companies threatened: senior data engineers who pride themselves on data modeling, Snowflake - automated data analysis by heavy LLM models Companies threaten: Accenture, Tata Consultancy Services, Infosys, McKinsey & Company - visualization frontends simplified and more customized Companies threatened: Tableau, Salesforce, Preset - sprint management done with agents Companies threatened: Atlassian, Linear, Notion The transformation we’re seeing is really intense. I’m excited to see where things end up! What else do you see going away or changing a lot?

Update!!! VisaPath UK is now live at visapath.co.uk — completely free to search all 125,572 verified sponsor companies. Check it out and let me know your feedback. Thanks

If you are a Software Engineer pivot to Cyber Software Engineering, Embedded Software Engineering, or FPGA Engineering.

*Finally* read through @samwhoo's blog on LLM quantization. It's incredible. For many (even in tech) the understanding of how LLMs work stops at the surface level. Sam is helping us all go deeper, digging into the interesting facets of how AI models truly work. Read it!

Zuckerberg paid $14.3 billion for a 28-year-old who had never trained a frontier model. Nine months later, that bet just shipped. The benchmark table tells you exactly what kind of lab Wang built. Muse Spark leads or ties Opus 4.6 and GPT 5.4 on multimodal perception, health queries, and visual reasoning. MedXpertQA, SimpleVQA, ScreenSpot Pro, CharXiv. These are all data-quality-sensitive benchmarks where training set curation determines the ceiling. Where it gets destroyed: ARC AGI 2 (42.5 vs 76.5 Gemini), Terminal-Bench (59.0 vs 75.1 GPT 5.4), GDPval office tasks (1444 vs 1672 GPT 5.4). Coding and abstract reasoning. The exact categories where architecture innovation and RL scaling matter more than data. This is a data labeling CEO's model. The fingerprints are all over the results. Wang spent seven years learning which benchmarks respond to better data and which ones require something else entirely. Muse Spark maxed out the first category and exposed the gap in the second. The $14.3B question was always whether the guy who built the best data pipeline in AI could build the best model. The answer so far: he built the best model at the things data pipelines solve, and a mediocre one at everything else. The move nobody's pricing: Meta said larger models are already in development, private API today, open-source future versions. Wang called this "step one." If the next model closes the coding and reasoning gap, Meta goes from also-ran to three-horse race. If it doesn't, they spent $14.3 billion to build a very good medical chatbot for 3 billion users. Both outcomes are interesting. Only one justifies the stock moving 9%.

Golden age of open source is here

All my Python projects are now using ty for type checks 🎉 @FastAPI, Typer, SQLModel, Asyncer, etc. Thank you @OxyKodit 🙌 ty is great, try it docs.astral.sh/ty/