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How to Build a Fully Local Thermal Printer Server (No Cloud Required)
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AI — Latest Models & Research
AI
How to Build a Fully Local Thermal Printer Server (No Cloud Required)
Build a local thermal printer server with a Raspberry Pi and Python — no cloud, no subscriptions. Step-by-step guide with ESC/POS, Flask, and systemd.
DEV Community
AI
How I stopped paying OpenAI to run my test suite
I was building an AI project and ran into something that kept bothering me. Every test that touched...
DEV Community
AI
How to turn any webpage into structured data for your LLM
Your LLM can reason, write code, and hold long conversations. Ask it to read a webpage and it falls...
DEV Community
AI
What 512K Lines of Leaked Claude Code Taught Me About AI Tool Design
Anthropic's Claude Code source leaked via npm source maps. I read the tool architecture. Here are 5 design decisions every AI tool builder should study.
DEV Community
AI
Freqtrade Tutorial 2026: How I Set Up a Crypto Bot That Hits 67.9% Win Rate
Step-by-step guide to setting up a Freqtrade crypto trading bot with multi-timeframe confirmation strategy achieving 67.9% win rate across 10,000+ trades.
DEV Community
AI
Why Most QA Engineers Can't Practice Their Core Skill — and How Mutation Testing Changes That
QA Engineers spend years on LeetCode sharpening algorithm skills — but that's not what QA is about. Mutation testing is the practice method that actually trains your bug-finding instincts.
DEV Community
AI
Show HN: ComputerPoker.ai – Learn GTO tournament poker strategy vs. GTO bots
ComputerPoker.ai is a website where users can play simulated poker tournaments against GTO Bots to learn GTO poker strategy in a fun and low-risk environment.<p>My motivation for creating CompterPoker.ai was feeling a bit overwhelmed by some of the professional poker tools out there for learning GTO play. For some tools, learning how to simply operate the tool itself felt like a second job. With ComputerPoker.ai players can play against bots themselves simulating GTO play to learn what it "feels like" to play GTO vs. GTO opponents without having to turn any knobs or dials (feedback is real-time as you play).<p>The Beta tester code for HN Users is: HackerNews2026. All feedback is welcome! Please send suggestions for improvement or bugs to contact@computerpoker.ai or alternatively leave a comment below. Any questions I will do my best to answer.<p>As for the product offering the website is designed to teach players how to play optimal poker strategy (GTO) in simulated Texas Hold 'Em poker tournaments. Our value proposition is that if you can consistently beat the bots then you will fare well in live poker tournaments (of course adjusting for your opponents' play).<p>In addition to GTO pre-flop quizzes and pre-flop charts, users have the ability to simulate poker tournaments from start-to-finish and get feedback on their decisions _in real-time_ in a fun and low-risk environment.<p>For those interested the tech stack is Django deployed on AWS via Terraform and SaltStack, the database uses a Postgres RDS backend, and the frontend uses HTMX with WebSockets via Django Channels and Redis (Nginx serving as reverse proxy with CloudFlare DNS and SSL). During the project I used Claude Code to aid with various boilerplate aspects of the code base including building out the repos for Terraform and SaltSack and of course speeding up Django development.<p>Users are graded pre-flop based on the covered pre-flop scenarios (two-ways only for now). Post-flop users are graded on a residual MLP PyTorch model. We have built an in-house solver in Rust using the discontented CFR++ algorithm. The PyTorch model approximates GTO play post-flop (again only two-ways currently) based on training data with raises, EV, and realistic ranges for OOP and IP players. Because the post-flop decisions are based on a model that will always be a work in progress I refer to these decisions as GTOA (or "GTO Approximate").<p>Version 8 of the PyTorch model is the first one that I am happy with and actually find it quite difficult to play against. If you manage to beat the bots please do let me know how many tries it took! For those curious the PyTorch params for the most recent run are below (I trained on a gaming PC via Linux WSL2 using an AMD GPU):<p>```
(venv_rocm_native) $ python3 pytorch_model.py
--data-dir unified_v8_raises/
--aggression-embed --predict-ev
--d-card 64 --d-model 256
--n-attn-layers 3 --n-trunk-layers 4
--dropout 0.2 --weight-decay 0.05
--epochs 30 --batch-size 4096 --lr 3e-4
--steps-per-epoch 10000 --output gto_model_v8.pt<p>Training on cuda
… … … _30 epochs later_
Saved best model (val_loss=0.3865)
Training complete!
Best model saved to gto_model_v8.pt
```<p>The website is live in Beta mode as I gather feedback on how things are structured and work out any bugs/kinks. If you have any suggestions for improvements I’d love to hear them. Subscriptions are live so if anyone wanted to test the Stripe payment processing flow I certainly wouldn’t mind! ;-)<p>p.s. This is a side gig for me. I am currently looking for full-time work either fully remote or on-site based in London, UK (this LLC that runs ComputerPoker.ai operates out of USA but I am based full-time in the UK and authorized to work in both UK and USA). If you or someone you know is looking for a SRE with strong software engineering skills please let me know!
Hacker News
AI
HippoCamp: Benchmarking Contextual Agents on Personal Computers
We present HippoCamp, a new benchmark designed to evaluate agents' capabilities on multimodal file management. Unlike existing agent benchmarks that focus on tasks like web interaction, tool use, or software automation in generic settings, HippoCamp evaluates agents in user-centric environments to model individual user profiles and search massive personal files for context-aware reasoning. Our ben
arXiv
AI
Universal YOCO for Efficient Depth Scaling
The rise of test-time scaling has remarkably boosted the reasoning and agentic proficiency of Large Language Models (LLMs). Yet, standard Transformers struggle to scale inference-time compute efficiently, as conventional looping strategies suffer from high computational overhead and a KV cache that inflates alongside model depth. We present Universal YOCO (YOCO-U), which combines the YOCO decoder-
arXiv
AI
LAtent Phase Inference from Short time sequences using SHallow REcurrent Decoders (LAPIS-SHRED)
Reconstructing full spatio-temporal dynamics from sparse observations in both space and time remains a central challenge in complex systems, as measurements can be spatially incomplete and can be also limited to narrow temporal windows. Yet approximating the complete spatio-temporal trajectory is essential for mechanistic insight and understanding, model calibration, and operational decision-makin
arXiv
AI
The Recipe Matters More Than the Kitchen:Mathematical Foundations of the AI Weather Prediction Pipeline
AI weather prediction has advanced rapidly, yet no unified mathematical framework explains what determines forecast skill. Existing theory addresses specific architectural choices rather than the learning pipeline as a whole, while operational evidence from 2023-2026 demonstrates that training methodology, loss function design, and data diversity matter at least as much as architecture selection.
arXiv
AI
$\texttt{YC-Bench}$: Benchmarking AI Agents for Long-Term Planning and Consistent Execution
As LLM agents tackle increasingly complex tasks, a critical question is whether they can maintain strategic coherence over long horizons: planning under uncertainty, learning from delayed feedback, and adapting when early mistakes compound. We introduce $\texttt{YC-Bench}$, a benchmark that evaluates these capabilities by tasking an agent with running a simulated startup over a one-year horizon sp
arXiv