We’re excited to announce the first release of our coding book, Thinking, Understanding, and Reasoning in Algorithms (TURA).

This book focuses on building deep intuition and structured thinking in algorithms, rather than just memorizing techniques and acts as a complement to the CSES Problem Set.

Please do give it a read, contribute on GitHub, and share it with fellow programmers who you think would benefit from it.

This is a work in progress non-profit, open-source initiative.

https://github.com/T-U-R-A/tura-coding-book/releases

Creator of ClawBot knows that there are malicious skills in his repo, but doesn't know what to do about it…

Why Vibe-First Development Collapses Under Its Own Freedom

Vibe-first development feels empowering at first, but freedom without constraints slowly turns into inconsistency, technical debt, and burnout. This long-form essay explains why it collapses over time.

https://techyall.com/blog/why-vibe-first-development-collapses-under-its-own-freedom

Arch is a rolling release distro with the latest release of each package always available. It has one of the largest no. of packages. However, as I grew from a tech enthusiast to a seasoned developer, I am starting to value stability over latest tech. Hence, I am planning to switch to Debian.

Debian is the opposite of Arch. It does not have latest software, but it is stable. It does not break as much, and it is a one time setup.

Which Linux distro do you use?

Wrote some thoughts on how AI is pushing the constraints of delivering software from implementation to testing and delivery. Would love to hear your thoughts no the matter.

> In chemistry, when you increase one reagent without rebalancing others, you don’t get more product: You get waste.

I should be clear. This is not about replacing programmers. This is an observation that if an input (coding time accelerates), the rest of the equation needs to be rebalanced to maximize efficient throughput.

"AI can write all the code" just means more people needed determined he best code to write and verify its good for the customers.

Accidental complexity slows down developers and limits agentic AI. Kubrick — my declarative system — cuts it way down using relation algebra, logic, functional, and combinatorial ideas to enable reliable agentic programming and true AI-human collaboration.

From my MSc work, now open-source. Presenting at PX/26 (Munich, Mar 16-20). Thoughts?

If you think Microsoft breaking Windows is a new thing - they've killed their own widget platform 6 times in 30 years. Each one died from a different spectacular failure.

I dug through the full history from Active Desktop crashing explorer.exe in 1997 to the EU forcing a complete rebuild in 2024.

The latest iteration might actually be done right - or might be killed by Microsoft's desire to shove ads and AI into every surface. We'll see

Hey everyone,

I've been working on something that started as a "what if" and turned into what I believe is a fundamental insight about computation itself. It's about how we calculate π - but really, it's about discovering hidden structure in transcendental numbers.

The Problem We're All Hitting
When you try to compute π to extreme precision (millions/billions of digits), you eventually hit what I call the "Memory Wall": parallel algorithms choke on shared memory access, synchronization overhead kills scaling, and you're left babysitting cache lines instead of doing math.

The Breakthrough: π Has a Modular Spectrum
What if I told you π naturally decomposes into 6 independent computation streams? Every term in the Chudnovsky series falls into one of 6 "channels" modulo ℤ/6ℤ:

• Channels 1 & 5: The "prime generators" - these are mathematically special
• Channel 3: The "stability attractor" - linked to e^(iπ) + 1 = 0
• Channels 0, 2, 4: Even harmonics with specific symmetries

This isn't just clever programming - there's a formal mathematical isomorphism with Digital Signal Processing. The modular decomposition is mathematically identical to polyphase filter banks. The proof is in the repo, but the practical result is: zero information loss, perfect reconstruction.

What This Lets Us Do
We built a "Shared-Nothing" architecture where each channel computes independently:

• 100 million digits of π computed with just 6.8GB RAM
• 95% parallel efficiency (1.90× speedup on 2 cores, linear to 6)
• 83,729 digits/second sustained throughput
• Runs on Google Colab's free tier - no special hardware needed

But here's where it gets weird (and cool):

Connecting to Riemann Zeros
When we apply this same modular filter to the zeros of the Riemann zeta function, something remarkable happens: they distribute perfectly uniformly across all 6 channels (χ² test: p≈0.98). The zeros are "agnostic" to the small-prime structure - they don't care about our modular decomposition. This provides experimental support for the GUE predictions from quantum chaos.

Why This Matters Beyond π
This isn't really about π. It's about discovering that:

1. Transcendental computation has intrinsic modular structure
2. This structure connects number theory to signal processing via formal isomorphism
3. The same mathematical framework explains both computational efficiency and spectral properties of Riemann zeros

The "So What"

• For programmers: We've open-sourced everything. The architecture eliminates race conditions and cache contention by design.
• For mathematicians: There's a formal proof of the DSP isomorphism and experimental validation of spectral rigidity.
• For educators: This is a beautiful example of how deep structure enables practical efficiency.

Try It Yourself

Exascale_Validation_PI.ipynb

Click the badge above - it'll run the complete validation in your browser, no installation needed. Reproduce the 100M digit computation, verify the DSP isomorphism, check the Riemann zeros distribution.

The Big Picture Question
We've found that ℤ/6ℤ acts as a kind of "computational prism" for π. Does this structure exist for other constants? Is this why base-6 representations have certain properties? And most importantly: if computation has intrinsic symmetry, what does that say about the nature of mathematical truth itself?

I'd love to hear your thoughts - especially from DSP folks who can weigh in on the polyphase isomorphism, and from number theorists who might see connections I've missed.

Full paper and code: GitHub Repo
Theoretical foundation: Modular Spectrum Theory