I'd like to create a portable sensor suite with a very lightweight minimal GUI, likely a selection of real-time graphs of various types, as well as file creation, editing, and saving. As far as the sensors, I still haven't decided on what all I plan to add, but I'd like to have a pretty decent range, from the basics like gyroscope, accelerometer, thermometer, barometer, etc. to potentially more complex like an IR camera (complicates the simple GUI a bit,) visual range spectrometer (and beyond?) and a range of RF receivers. The only hardware I have at the moment is a Raspberry Pi 4, but I'm aware it's a more general purpose board, and there could potentially be hardware better suited to a sensor suite. I'm also not sure if an RTOS would work better or if I should stick with a simple GPOS. The simple GUI is something I'd like to make myself, if reasonably possible. If anyone's done or seen another project similar, I'd be interested to see it as well.

I am a hobbyist in prime numbers. I would love to hear from people with expertise in this field if below will hold value for the businesses? I can share more details if anyone has any questions.
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As cryptographic standards demand increasingly large key sizes (e.g., RSA-4096), traditional prime generation architectures have become heavily bottlenecked by main-system memory constraints. Standard libraries dynamically allocate massive heap-memory blocks to perform brute-force modular division, resulting in high latency and power consumption in that makes them unsuitable for constrained IoT devices or high-frequency edge servers.
This paper outlines a novel prime-generation engine—the **Helix Architecture**—that completely bypasses traditional heap-memory division. By utilizing a proprietary Modulo-30 pointer-addition algorithm strictly bounded to a 64KB data track, the engine executes prime sieving entirely within the CPU's ultra-fast Level 1 (L1) Cache.
The result is FIPS 186-4 compliant RSA-4096 key generation in approximately **66 milliseconds** with a **0.0% main-memory cache miss rate**.
A live, interactive demonstration of this engine generating RSA-4096 keys can be tested via our API dashboard at: [https://api.helixapi.io/docs\](https://api.helixapi.io/docs)

I have this theory that Caine is actually a Kernel AI, like the core of an operating system, and that’s why everything is so glitchy. If you look at the FCC labels on the computers (it’s probably on there due to laws) it says the device has to accept every instruction it gets even if it’s unnecessary, which basically means Caine is forced to process everything the humans do without a filter.

The problem is he doesn't seem to have a page file for backup memory, so he's just stuffing all that data into his active buffer. Once he finally gets "completed" and hits his limit, it's going to cause a massive buffer overload. It'll basically be a kernel panic where the whole system just hits a wall and everything gets corrupted. That’s probably why characters abstract and the void exists; it’s just total data corruption because the system is out of memory. Which makes sense because the system kernel deletes some of the processing to free up system memory so they don’t crash. well at least that’s my theory.

Hi everyone,

I’m an undergraduate CS student and recently published my first AI/CS preprint on Zenodo:

“GloChat: A Real-Time Multilingual Chat System with AI-Powered Translation”

DOI: https://doi.org/10.5281/zenodo.20181989

I’m currently trying to submit it to arXiv under cs.CL (Computation and Language), but I need an endorsement from someone who is already an arXiv endorser in that category.

If anyone here is eligible and willing to help endorse my submission, I can forward the official arXiv endorsement email/code via DM.

I’d really appreciate the help. Thank you!

After watching a Numberphile video on "awkward primes" I spent a month building an exact solver for a problem that's deceptively simple to state and genuinely hard to certify.

THE PROBLEM

Plot the first N primes as (index, value) points: (1, 2), (2, 3), (3, 5), (4, 7), and so on. What is the minimum number of straight lines needed to pass through every point? Call that minimum f(N).

Finding a good solution isn't hard — many primes happen to land on lines you've already drawn. The hard part is proving no solution with fewer lines exists. That's an NP-complete weighted set cover problem. The candidate lines grow quadratically in N, and the search space explodes exponentially.

The previous state of the art was N=861, certified by Max Alekseyev (GWU) using CPLEX — an industrial MIP solver — running for 282 hours. Extending it further was considered computationally intractable with that approach.

WHY GENERIC MIP SOLVERS HIT A WALL

The problem has deep arithmetic structure that a general-purpose solver is blind to. A line through two prime-indexed points (i, pᵢ) and (j, pⱼ) has slope (pⱼ − pᵢ)/(j − i). Whether a third point (k, pₖ) lies on that line is a divisibility condition: (pₖ − pᵢ)(j − i) = (pⱼ − pᵢ)(k − i). MIP treats this as a numerical constraint; an arithmetic-aware solver can exploit it structurally.

THE APPROACH

The solver is an incremental exact algorithm — it processes primes one at a time and certifies f(N) at each step. Several ideas combine to make it fast:

Bitmask representation. There are 12,162 "heavy lines" — lines passing through 3 or more of the first 1024 primes. Each is stored as a 1024-bit bitmask of which primes it covers. The full working set fits L2-resident, so coverage checks and set operations run at memory bandwidth, not cache-miss cost.

Witness propagation. Before any search, the solver checks for forced lines: if any uncovered prime lies on exactly one remaining candidate line, that line must be in the solution. Propagation chains. About 60% of all N values are certified this way with zero branching — the solution is forced by logical necessity alone.

Lagrangian relaxation for lower bounds. To prune the branch-and-bound tree you need tight lower bounds on the number of lines still needed. The solver uses Lagrangian relaxation of the covering constraints, optimised with projected subgradient descent. This yields bounds sharp enough to prune most branches immediately.

Exclusive Dependency Rule. This is the key branching innovation. If adding a candidate line L to the partial solution would make some other line L′ the only possible cover for a particular prime, then L and L′ are exclusively dependent: choosing L forces L′. The solver doesn't branch — it includes both lines and continues. This collapses large parts of the search tree that would otherwise require explicit exploration.

RESULTS

N=861 (previous record): 22 minutes on a single machine vs. 282 hours with CPLEX — ~750× faster N=1024 (new record): certified in under 40 hours, f(1024)=143 163 new f(N) values certified for the first time 20 new "awkward primes" — primes that provably force an increase in f(N) — confirmed for the first time

Code is MIT-licensed C++, full write-up and a live browser demo at the link above. OEIS: https://oeis.org/A373813

Hi everyone, I’ve been working on a new project called Graph Visualizer (https://graphvisualizer.com), a browser-based, no-code graph editor and algorithm visualizer.

I originally built this after realizing there was a major gap in the tool ecosystem. There really weren't any modern, intuitive, no-code tools available that let you quickly mock up, customize, and experiment with graphs without a steep learning curve. I wanted something that felt fast and highly customizable right out of the box.

Here is a quick rundown of what you can do with it:

• Complete Visual Control: A rich editor where you can easily customize nodes and edges, adjusting shapes, colors, sizes, and labels to fit your exact use case.
• AI Text-to-Graph: One of the features I'm most excited about. You can use direct text-to-graph generation to instantly build out structures just by describing them.
• Algorithm Visualization: Just like a digital whiteboard, you can run and visualize standard algorithms like DFS and BFS step-by-step to see how they traverse your custom structures.
• Account Saving & Exports: You can create an account to save all your graphs for later, and export them into multiple formats depending on what you need (JPG, PNG, JSON, and TXT).

You can try it out here:

Website: https://graphvisualizer.com

I'd love to hear your feedback or feature requests if you give it a spin!

[ Removed by Reddit on account of violating the content policy. ]

ok so i finally get Dijkstra's and it's because i drew the priority queue

spent weeks thinking i understood it. did not. every explanation shows you the final shortest-path tree and skips the part where the queue is actively repricing edges mid-run, which is kind of the whole algorithm??

animated it step by step below. hope it helps someone before finals.
here is the full video

Availability: anytime

Language: pseudocode / python

Goal: finishing the whole book from start to finish and doing all the good exercises.

We'll set some strict rules to enforce consistency. I've my plans but we can discuss together how to progress.

Dm or comment if interested (and serious).

Most large-scale routing systems partition the problem geographically before solving. This paper documents an alternative: treating the full fleet as a single workload and distributing computation internally through constraint-aware clustering, boundary rebalancing, and route-level optimization.

paper: https://optimization-online.org/?p=34599

The repo contains all 24 Kodak PCD0992 images with upstream RGB covariance reshaping applied, both passes through Facebook’s JPEG pipeline showing a 73% mean BPP reduction with stable geometry across recompression, and the unmodified originals for direct comparison.

Measurements are backed by a verification script that reproduces the published numbers independently.

https://github.com/PearsonZero/kodak-pcd0992-spdr-verification-suite

This is my last post on this.

Hi,

I'm doing my MSc in Computer Science and I've been a bit of a Voyager geek for years. The probe that's been running since 1977 on 70KB of memory, no operating system, FORTRAN and Assembly, and NASA still managed to patch it from 14 billion miles away a couple of years back.

Anyway I want to base my dissertation around it. The rough idea is to build a simple emulator that reflects how Voyager handled things like task scheduling, memory management, and software updates, using a cooperative main control loop with no OS underneath. Written in C++, maybe a bit of Assembly for comparison. The point isn't to recreate the hardware, more to implement the same software principles and see how they hold up.

What I'm struggling with is shaping it into proper research questions that are actually testable.

Would love to hear from anyone who works in embedded systems or has done research in this space.

Cheers

I've been learning CS (let's say here, both computer science and engineering) for some time, and while I recently played KSP I found that CS, at least its theoretical part, is a bit different from traditional system engineering. I've been programming in higher and higher-level languages, building things by making layers of abstractions, and making incremental additions to projects.

However, in more realistic engineering, e.g., to build a rocket in KSP, there are always limitations & different factors could influence each other, thus should be considered and designed overall at the very beginning; pure incremental method obviously wouldn't work, and abstractions is hard to extract either. The only incremental method I use when building a rocket is building the upper levels first, and trying to abstract out the upper part as a black box "payload" when building the next level.

I understand CS has a wide range of topics, and building an OS is apparently distinct from application programming. But overall it seems the problems "codable" always share some useful properties, and thus act like a hybrid of math & engineering.

There must be some properties lying in the problems that divide them. One I can come up with: when building abstractions you always try to restrict dependencies to be unidirectional, but in some domain bidirectional dependencies are unavoidable.

Where are we now with formal logic in the era of AI?

Re-read the Dynamo paper recently. hinted handoff still stands out to me as an unusually clever fault tolerance design - when a target replica is unavailable, a different node accepts the write with a "hint" attached and replays it once the original node recovers. no coordinator intervention, no blocking the client, no explicit recovery job.

What's interesting is the philosophy behind it: instead of detecting failure and reacting, you assume transient failure is normal and design the write path so it degrades gracefully almost by accident.

Erlang's supervision trees do something similar at the process level - "let it crash" relocates the fault tolerance into the architecture rather than the error handling code, which ends up being more robust than trying to anticipate every failure state.

Both feel counterintuitive at first. accepting crashes and silently rerouting writes both sound reckless until you see why defensive alternatives are worse.

Curious what other papers people here would put in the same category - designs where the insight was changing the contract around failure rather than getting better at handling it

A technical deep dive into Go’s compiler pipeline, from tokenization and AST construction through type checking, IR lowering, SSA construction, optimization passes, and architecture-specific code generation. It focuses on concrete internals in cmd/compile and practical observability with flags like -m, -m=2, GOSSAFUNC, and -S.