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Cartpole, LunarLander, BiPedal Walker
Applied reinforcement learning for agent to play these 3 games: Cartpole, LunarLander, BiPedal Walker. We used the basic model of Environment -> State -> Agent -> Action to train our agent. We reward the agent for achieving an outcome that we want, while penalizing the agent for doing otherwise. After many iterations, our agents learns to clear the games.
Category tags:Simple Video captioner
A two-stage video captioning agent, built reliability-first. Stage one samples keyframes from each clip and sends them to a vision-language model (Kimi K2.6 on Fireworks), which returns a dense, factual description — this is what the accuracy score is made of. Stage two turns that description into the four requested styles as a single JSON object — this is what the style score is made of. Splitting them means a caption can only be as accurate as what was actually seen, and the pressure to be funny never contaminates the grounding. Everything underneath is about not failing. A complete, valid results.json is written before the first network call, then each clip is atomically replaced as it finishes, so a crash or a hang still leaves a scoreable file behind. The agent enforces its own 540-second deadline and exits cleanly rather than waiting to be killed. Each stage walks a fallback ladder — API, then local model, then template — so no single failure is fatal. In testing, a real local-model outage still produced twelve valid captions with 292 seconds to spare. The local model is the part I most wanted to build. I distilled the teacher's style behaviour into Gemma 4 with LoRA on teacher-labelled examples, merged the adapter, and quantized it to a Q4_K_M GGUF — 5.3 GB, 2.86x smaller than bf16 — served by llama-server on CPU inside the container, no GPU required. It styles a clip in about 30 seconds on two cores. It ships as the fallback rather than the primary, because I measured instead of assuming. A blind, order-swapped LLM judge (n=19, minimum detectable effect +/-0.44) found the LoRA bought style (+0.61) but cost accuracy (-0.29), and the teacher still beat the student 10-3 on swap-surviving verdicts. The same judge found Q4_K_M statistically indistinguishable from bf16 — 4-bit quantization was free. So the distilled model earns its place by surviving an outage, not by going first.
Neotron
AIVE-Artificial Intelligence Venture Engine
AIVE (Artificial Intelligence Venture Engine) is a Cognitive Discovery Operating System that transforms documents, research papers, and data into structured knowledge, evidence-backed insights, dynamic reasoning, and actionable innovation opportunities.
Quacky Wonderland
SOAP Copilot: AI Clinical Scribe on AMD
SOAP Copilot turns raw doctor-patient conversations into structured SOAP notes, ICD-10 codes, and patient-friendly summaries in seconds, using a 3-agent Llama 3.3 70B pipeline built and fine-tuned on AMD hardware.
LoneSoloWolf
SmartBandit Router
A reinforcement-learning router that learns which AI model to call for each query, cutting cost by ~93% without ever hardcoding a routing rule.
nyan
Thymus
Thymus is a lightweight hybrid token-efficient router designed to maximize accuracy while minimizing token costs in multi‑task LLM pipelines. It dynamically routes user queries across local and remote models on LLM providers.
The Disappointer
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For Innovators & Creators
Simple Video captioner
A two-stage video captioning agent, built reliability-first. Stage one samples keyframes from each clip and sends them to a vision-language model (Kimi K2.6 on Fireworks), which returns a dense, factual description — this is what the accuracy score is made of. Stage two turns that description into the four requested styles as a single JSON object — this is what the style score is made of. Splitting them means a caption can only be as accurate as what was actually seen, and the pressure to be funny never contaminates the grounding. Everything underneath is about not failing. A complete, valid results.json is written before the first network call, then each clip is atomically replaced as it finishes, so a crash or a hang still leaves a scoreable file behind. The agent enforces its own 540-second deadline and exits cleanly rather than waiting to be killed. Each stage walks a fallback ladder — API, then local model, then template — so no single failure is fatal. In testing, a real local-model outage still produced twelve valid captions with 292 seconds to spare. The local model is the part I most wanted to build. I distilled the teacher's style behaviour into Gemma 4 with LoRA on teacher-labelled examples, merged the adapter, and quantized it to a Q4_K_M GGUF — 5.3 GB, 2.86x smaller than bf16 — served by llama-server on CPU inside the container, no GPU required. It styles a clip in about 30 seconds on two cores. It ships as the fallback rather than the primary, because I measured instead of assuming. A blind, order-swapped LLM judge (n=19, minimum detectable effect +/-0.44) found the LoRA bought style (+0.61) but cost accuracy (-0.29), and the teacher still beat the student 10-3 on swap-surviving verdicts. The same judge found Q4_K_M statistically indistinguishable from bf16 — 4-bit quantization was free. So the distilled model earns its place by surviving an outage, not by going first.
Neotron
AIVE-Artificial Intelligence Venture Engine
AIVE (Artificial Intelligence Venture Engine) is a Cognitive Discovery Operating System that transforms documents, research papers, and data into structured knowledge, evidence-backed insights, dynamic reasoning, and actionable innovation opportunities.
Quacky Wonderland
SOAP Copilot: AI Clinical Scribe on AMD
SOAP Copilot turns raw doctor-patient conversations into structured SOAP notes, ICD-10 codes, and patient-friendly summaries in seconds, using a 3-agent Llama 3.3 70B pipeline built and fine-tuned on AMD hardware.
LoneSoloWolf
SmartBandit Router
A reinforcement-learning router that learns which AI model to call for each query, cutting cost by ~93% without ever hardcoding a routing rule.
nyan
Thymus
Thymus is a lightweight hybrid token-efficient router designed to maximize accuracy while minimizing token costs in multi‑task LLM pipelines. It dynamically routes user queries across local and remote models on LLM providers.
The Disappointer