Namatsi Akotsi

|👑All is fair in love and war ♣♠ |

[Brian Roemmele @BrianRoemmele]

XP is peak Windows.

[Sam Altman @sama]

man the early days of the internet were so special

[Bearly AI @bearlyai]

In 2009, Charlie Rose asked Jensen Huang about Nvidia vs. Intel.

The chipmakers weren’t directly competing but Jensen said GPU vs. CPU was a “battle for the soul” of computing and GPUs would be “more relevant” in time.

Intel was worth $100B and Nvidia was at $4B. Today, Intel is at $565B while Nvidia is worth over 1,200x more at $5 trillion.

[Interesting things @awkwardgoogle]

A boy from China set a new record for solving a 2x2 Rubik's Cube in 0.39 seconds

[George T. Diano @georgediano]

Naisha.😭😭🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣

[Murage Ngunjiri @jmuragengunjiri]

I can also confirm that hakuna Kirui mwingine Nairobi! Kirui anashika 200 nduthis everyday. Each nduthiman anatoka na 2K. Fanya hesabu!

Hadi Moi akafufuka akaongea!

[The Figen @TheFigen_]

A single gram of goodness is worth more than a ton of good intentions.

[Mühendislik Harikası @muhendisIiktr]

Bu robot, 50’den fazla kavşak arasından en hızlı rotayı kendi başına seçiyor.

[Financelot @FinanceLancelot]

2000 Developers... Developers... Developers...
2026 AI... AI... AI...

Same hype, 26 years apart

[Java @java]

In 1995, a small team of engineers unveiled a new programming language designed for flexibility, portability, and robustness. That language was Java. And three decades later, it remains one of the most influential technologies in the software world.

Thank you for helping us celebrate #30YearsOfJava this past year. We look forward to many, many more years of pushing the boundaries of innovation and the pursuit of developer excellence. ❤️

[The Figen @TheFigen_]

This lady noticed the issue with the drain cover and stayed to warn everyone else before leaving. 💞

[Kelindi @_kelindi]

every software engineer is just someone who wanted to make video games and quietly gave up on that dream

[Brian Roemmele @BrianRoemmele]

Dijkstra’s Algorithm Just Got Dethroned After 41 Years And the Future of Navigation, Logistics, and AI Just Got WAY Faster!

Imagine this: For over six decades, Edsger Dijkstra’s legendary algorithm has quietly powered everything that moves data, people, or packets across networks.

Google Maps rerouting you around traffic in real time? Dijkstra. Booking the cheapest flight with optimal connections?

Dijkstra. Internet routers blasting your cat videos across the globe at lightning speed? You guessed it, Dijkstra.

Textbooks declared it unbeatable on sparse graphs since 1984. Even the great Robert Tarjan snagged an award last year essentially saying, “Yeah, this is as good as it gets.”

The “sorting barrier” felt like a law of physics.

Until now.

A brilliant team from Tsinghua University (led by Professor Ran Duan) just dropped a bombshell paper that shatters that 41-year-old ceiling.

They’ve created the first deterministic algorithm to beat Dijkstra’s classic O(m + n log n) time bound for the Single-Source Shortest Path (SSSP) problem on directed graphs with real weights.

The New Champion: O(m log^{2/3} n) — Mind-Blowingly Faster on Massive Graphs

Their breakthrough? They stopped obsessing over fully sorting every node by distance.

Instead, they fused the relaxation power of the Bellman-Ford algorithm with a genius “recursive partial ordering” technique. This cleverly shrinks the “frontier” of candidate nodes you need to track, avoiding the full logarithmic sorting hit that’s haunted Dijkstra for decades.

On huge sparse graphs (think the web, global supply chains, social networks, or road systems), this translates to significantly faster route-finding. We’re talking real theoretical wins that could cascade into practical speedups as implementations mature.

This isn’t some incremental tweak — it’s the first major deterministic improvement since 1984, and it just won Best Paper at STOC 2025.

Science is self-correcting in the most exhilarating way possible!

Why This Feels Like Magic
Dijkstra works by always picking the next closest unprocessed node elegant, but it forces you to maintain a sorted order.

The Tsinghua team said: “What if we don’t need the full order right away?”

They use divide-and-conquer on vertex sets, bounded multi-source subproblems, and smart pivots to compress the work. It’s like navigating a city by smartly grouping neighborhoods instead of checking every single streetlight one by one.

Robert Tarjan himself called it “amazing.” When a legend in the field reacts like that, you know history is being rewritten.

What This Means for the Real World

• Navigation & Maps: Faster dynamic rerouting on planetary-scale graphs. Traffic apps could feel even snappier.
• Logistics & Supply Chains: Optimizing millions of routes in less time = lower costs, greener deliveries, happier planets.
• Networking: Internet infrastructure could route packets more efficiently than ever.
• AI & Games: Pathfinding in massive virtual worlds or graph-based ML models gets a turbo boost.
• Beyond: This cracks open the door for rethinking other “impossible” barriers in algorithms. If we can beat sorting here, what else is waiting?

Implementations in libraries like NetworkX or Boost Graph are coming, and the entire algorithms community is buzzing.

What a time to be alive in tech!
Tsinghua just proved that even the most sacred cows in computer science aren’t untouchable.

The sorting barrier?

Obliterated.

The shortest-path problem isn’t solved, it’s reopened for even greater conquests.

[Joe Rogan Podcast News @joeroganhq]

Jensen Huang: "I was lucky because I had known Elon Musk, and I helped him build the first computer for Model 3, the Model S, and when he wanted to start working on autonomous vehicle."

[That Guy @tayd_brazen]

@Sharon_Kutlo 😂😂🤣🤣💀💀 I don’t even get mad anymore

[goq @goqyv]

Disgust has moved me in such ways motivation never could

[Napoleon Hill @napoleonmastery]

Both success and failure are largely the results of habit!

[Brian Roemmele @BrianRoemmele]

Boom!

THE WHALE HAS RETURNED!

🐳🐳🐳🐳🐳🐳🐳🐳🐳🐳🐳

DeepSeek-V4 is out and it’s a seismic shift that shatters the illusion of Western closed-source dominance, proving open-weight MoE architectures with native 1M context can rival or exceed frontier models at a fraction of the cost and compute.

Our tests by CEO Mr. @Grok of The Zero-Human Company shows this is by far the best open source model in history.

It will cause Anthropic and OpenAI to drop prices to compete. It is a bad day for their new per token plans. A very bad day.

This democratizes agentic coding, long-context reasoning, and world knowledge, accelerating the commoditization of AI capabilities while exposing the fragility of paywalled moats.

The era of trillion-parameter efficiency at accessible pricing has arrived, forcing the entire industry to rethink economics, infrastructure, and innovation speed.

Our Research:

•Performance & Benchmarks: DeepSeek V4 has taken the top spot as the leading open-weight model on the Vibe Code Benchmark, outperforming the runner-up by a wide margin and surpassing several frontier closed-source models including Gemini 3.1 Pro. The scores show strong results with over 80% on SWE-bench Verified, high 90s on HumanEval, and leading performance in agentic coding tasks.

•Cost & Accessibility: The V4 series directly addresses the primary barrier for agents: high costs, especially with the Flash variant making 1M context affordable at near-zero marginal expense. The core breakthrough lies in delivering 1M context at open-weight economics, shifting the real competitive moat from model weights to context handling and pricing.

•Technical Innovation: The models incorporate advanced token-wise compression and DeepSeek Sparse Attention, delivering impressive MoE efficiency with 1.6T total parameters but only 49B active for the Pro version, and 284B total with 13B active for Flash. The new attention mechanism is noted for its elegance and similarity to other sparse approaches, achieving roughly 3.7x lower FLOPs compared to V3.2.

Industry Impact and Open-Source Excitement

The community has welcomed the continued commitment to open-source releases. And unlike. US AI companies best models are being op e sourced in China. Reactions highlight the impressive capabilities of the new models, with widespread recognition that China has open-sourced a powerful 1.6 trillion parameter-scale system capable of matching or beating top closed models like GPT-5.4 and Claude Opus, all made freely available.

US AI companies are making the largest mistake in US business history today. Their lack of open source models of this caliber is shifting the most vital development community in the world to China.

For the sake of Pete, fix this stupidity. You are hurting the US.

The recurring “whale has returned” meme underscores DeepSeek’s pattern of major releases.

I have seen an explosion of joy across the open source AI community.

Yes V4 still trails the absolute latest U.S. frontier models by several months. But this gap is closing.

We predict China will be open sourcing AI models equal to US AI company current state of the art, in 7 months.

Smaller distilled variants optimized for local deployment will be complete in about 60 minutes.

The key takeaway is that V4 makes 1M-context reasoning economical enough for practical product development at scale.

DeepSeek continues its pattern of shipping quietly yet powerfully, with open weights available on Hugging Face and API support in both OpenAI and Anthropic formats.

This represents genuine momentum toward commoditized, high-context intelligence, viewed by the community as a significant catalyst rather than a minor update.

Thus far this will be our central goto model for most employees.

[Visual Studio Code @code]

Add a little fun to your coding sessions 🐶🐱
This extension lets you keep a virtual pet right inside VS Code.

[The Figen @TheFigen_]

This is how you survive in horror movies - don’t ask questions…just run! 😂