June 2026

Benchmarking Self-Hosted Gemma 2 9B vs. Frontier APIs: The FP8 Quantization Prefill Tax and VRAM Realities on an NVIDIA L4 [P]

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When evaluating migrating production LLM workloads off commercial cloud APIs, the conversation usually gets oversimplified into a trade-off between quality and infrastructure cost. To look past clean, isolated averages, I built a repeatable evaluation matrix using a real-world workload: cold outreach and contextual profile re-engineering for my resume generation platform. I benchmarked an unquantized Gemma 2 9B against an optimized FP8 variant served via vLLM on a single commodity NVIDIA L4 GPU. The dataset evaluates dynamic text generation […]

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I implemented Tabular Q - Learning from scratch

I implemented Tabular Q – Learning from scratch

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Check out the GitHub repo. This is a from scratch implementation of Tabular, 1-step, Q – Learning, with the environment built from Pygame. The above GIF demonstrates the agent exploring/exploiting the environment (left) based on the epsilon value to maximize it’s reward signal, and the Q – function (right) displaying what the agent thinks is the action per state that yields the highest reward for that state. submitted by /u/compugineer44 [link] [comments]

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I built Reinforcement Learning Map

I built Reinforcement Learning Map

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I built a free handbook where the entire field is laid out as an interactive map — ~25 algorithms grouped into branches (value-based, policy-based, model-based, planning), and clicking any node takes you to a full chapter with the intuition, math, and runnable code. Site: rl-handbook.com Code: github.com/lubludrova/rl-handbook Would really appreciate feedback — especially where explanations are unclear or where you’d want more depth. What topics should I prioritize next? submitted by /u/Savings-Shoulder-976 [link] [comments]

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MathFormer: Testing whether symbolic math is pattern matching or reasoning [D]

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Repo link and results – https://github.com/Abhinand20/MathFormer Task: Given a factorized expression like (7-3*z)*(-5*z-9), predict the expanded form -> 15*z*2-8*z-63 Key takeaway: A tiny (4M param) seq2seq model trained with no math knowledge reaches ~98.6% accuracy on symbolic math tasks, suggesting it learns structural token transformations rather than any notion of operators or variables. Scaling this up could help explain why LLMs appear to “reason” mathematically, when they may actually be performing large-scale structured pattern completion. How does RL […]

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When will the decimals in a/b repeat?

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The previous post looked at how many digits are in the reduced fraction for the nth harmonic number. I was curious about how long the cycle of digits in a harmonic number might be. I wrote about the period length for the digits of fractions almost a decade ago. This post includes code so I can apply it to harmonic demoninators. from sympy import lcm, factorint, n_order def period(n): factors = factorint(n) exp2 = factors.get(2, 0) exp5 = factors.get(5, […]

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Cognicore

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We’ve been building CogniCore, an open-source memory and reasoning infrastructure for AI agents, and one result I wanted to share is that CogniCore reached ~95% on LongMemEval while we’ve been developing the system. pip install cognicore-env CogniCore is focused on a pretty simple problem: most agent systems still treat every run like a fresh start. We’re trying to build the opposite infrastructure for agents to store useful experience, retrieve it when relevant, reflect on failures, replay past attempts, […]

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Hiding messages in the least significant mantissa bits of fine-tuned ONNX model weights [P]

Hiding messages in the least significant mantissa bits of fine-tuned ONNX model weights [P]

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Hey everyone, I’d like to share my project along with a short explanation of the process and why it came about in the first place. To start off, I’m not exactly the best at cryptography/steganography, in my case it’s always been something that sat in the background, as one of the sub-fields needed for another (main) field I’m actually interested in. For this project I tried to look up as much information as possible about what’s currently considered […]

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