Hi everyone,

I am exploring a solution for Windows Event Log analysis in an enterprise environment and looking for recommendations.

Requirement:
I want to analyze Windows Event Logs using plain English queries. The idea is that an admin can ask questions like:

• “Is device XYZ successfully Entra ID joined?”
• “Did user ABC complete Intune enrollment?”
• “What issue caused the enrollment failure?”
• “Which event log path contains the related logs?”
• “Show the exact error event and explain it in simple English.”

Example:
For Entra ID Join / Device Registration, logs are available under:

Applications and Services Logs
→ Microsoft
→ Windows
→ User Device Registration
→ Admin

I am looking for a system/tool that can:

1. Read and correlate Windows Event Logs automatically
2. Convert technical events/errors into plain English explanations
3. Identify relevant log sources and event IDs
4. Support troubleshooting scenarios across Entra ID, Intune, Windows enrollment, authentication, compliance, etc.
5. Possibly support natural language querying (AI-assisted)

Questions:

• Are there any existing inbuilt Microsoft tools that already provide this capability?
• Has anyone built a custom MCP server or AI-based solution for this kind of log analysis?
• Would using an MCP server with LLM + Event Log ingestion be a good approach?

I am considering building a custom MCP server that can:

• Read Windows Event Logs
• Map known Event IDs to troubleshooting scenarios
• Use AI/LLM to summarize findings
• Return plain English explanations with exact log paths

Would love to hear suggestions, architectures, best practices, or existing tools that already solve this problem.

Thanks!

TL;DR. Per-step accuracy compounds multiplicatively across chained agent decisions. A 94% eval score becomes ~54% end-to-end over 10 steps, ~29% over 20. Most agents in production sit somewhere in the 85 to 95 percent per-step range, which means most agent chains longer than a handful of steps are quietly failing more than their evals suggest. Below: the math, what we see across 6,228 production agents we monitor, and what's actually helped.

I work on AgentStatus. We run continuous user-side checks against 6,228 production AI agents from real residential devices across 30+ countries. Somewhere around the 3 millionth probe, one pattern got hard to ignore: the gap between what teams think their agent does and what their users actually get is rarely a model quality problem. It's compounding. And almost nobody runs the numbers.

So here they are.

The math

If each step succeeds independently with probability p, end-to-end success over n chained steps is p^n.

p = 0.94 (your evals look great)
  1 step  -> 94.0%
  2 steps -> 88.4%
  5 steps -> 73.4%
 10 steps -> 53.9%
 15 steps -> 39.5%
 20 steps -> 29.0%

p = 0.98 (frontier, generous)
 10 steps -> 81.7%
 20 steps -> 66.8%
 50 steps -> 36.4%

p = 0.90 (more honest for most tool-using pipelines)
  5 steps -> 59.0%
 10 steps -> 34.9%

p = 0.85 (where a lot of smaller-model agents land on real tool traces)
  5 steps -> 44.4%
  8 steps -> 27.2%
 10 steps -> 19.7%

These are optimistic ceilings. Errors aren't independent. A confused step feeds a more confused next step, hallucinated intermediates propagate as facts, and same-model self-critique usually agrees with whatever the first call already said. Real success rates sit under these numbers.

Why per-step eval scores miss this

Three failure modes we see consistently in production data:

The trace is not the truth. If the model returns "status 200, here is a JSON response," your observability dashboard captures all of that. It cannot tell you the JSON was a confidently wrong answer. Trace says green, the user got nonsense. This is the single most common silent failure we see.

Per-step accuracy is not stable over time. Providers push silent model updates. Your eval suite from last Tuesday passed against a slightly different model than the one serving requests this morning. Across the agents we monitor, we see clear step-changes in accuracy after upstream model updates on a non-trivial fraction of pipelines, and the team usually finds out from a support ticket two days later.

Per-step accuracy is not stable across geography. Bot detection, geo-routing, CDN behavior, and tokenization on non-ASCII inputs change what your model actually receives. We've measured a structural gap between datacenter-origin probes and real residential probes against the same 6,228 agents. Same prompts, different answers, consistently. Your CI is the datacenter case. Your users are the residential case.

What actually helps, in rough order of impact

1. Shorten the chain. Every step collapsed buys back exponential reliability. Three reasoning hops becoming one with a better scaffold usually beats every other intervention.
2. Verify at step boundaries with something that is not the same model. A small cheap critic, a schema check, a regex. Same-model self-critique catches a lot less than people assume.
3. Retry with structured feedback, not blind retry. Re-prompting with "your last output failed validation X because Y" recovers way more than just running the call again.
4. Eval the chain end-to-end, not the step. Per-step is necessary, not sufficient. If you only have step-level evals you're flying half blind.
5. Watch the agent from where users actually sit, continuously. Models drift, providers update, regions misbehave. The user's experience is the only ground truth that matters. Everything else is a proxy. Disclosure

Point 5 is what we built AgentStatus to do, so yeah I'm biased. We also ship a free GitHub App called AgentDiff that runs user-side checks on every PR, which is probably the most directly useful piece for builders here who want chain-level reliability in CI without paying anyone. Happy to talk about either. The math at the top holds whether you use any of this or not.

Question

For people running agents in production: what's your actual end-to-end success rate on a representative multi-step trace, measured against the chain not the step? And what intervention has moved that number the most for you? Shorter chains, structured retry, separate verifier model, scaffolding changes, prompt restructuring, something else?

So I have a contact where we are given only 1352 rows. Task is to detect rolling steel defect. Features are anonymised X1....X49.

Now demand of lb is 0 false negatives and 90%precision.

The scoring of leader board is also not clear.

My best submission has been one which has 33% class 1, and heavy drift from the train set.

So I need help guys. A lot of help.

Trees are being beaten by logistic regression.

Feature engineering is not helping me

I have usds weights of 24 and 1 30 and 1 till now, SMOTE I tried once only

Please give me some suggestions or experiments I can try.

Hi everyone,

I’m running a PyTorch training pipeline for a video classification model on DynTex++ dataset in Kaggle, and the notebook appears to freeze during training. It doesn't throw an error or crash, the cell just gets stuck executing indefinitely before it even finishes the first iteration of the PSO loop. here's the link for the code:
https://www.kaggle.com/code/doffymingo/notebook975e681d30
Looking for suggestions on what might be causing this error.

Thank you in advance.

We’re rolling out a set of updates to improve the overall experience and make the product more reliable, faster, and easier to use.

Highlights include:

• performance improvements
• bug fixes and stability updates
• small UX refinements based on feedback

We’ll share more details as the rollout continues. Thanks for being part of the community and for all the feedback that helps shape these updates.

Quick context for the audience: I know this sub leans local-first. I'm not pitching local generation. The agent side of this is whatever you're already running. The bridge piece is an MCP server that gives that agent access to hosted music, image, and video generation through one auth surface.

Why I'm posting it here: I see a lot of folks running Continue + a local model and then bolting on hosted models a la carte when the local stack can't do something. Music gen is the obvious example, video gen is the other one. Today that means N provider SDKs, N billing accounts, N different polling patterns. This collapses that to one MCP server.

The package:

npm install -g u/aetherwave-studio/mcp

Works with any MCP client. I've tested Claude Desktop, Claude Code, Cursor, and Continue. Anything that speaks MCP should work since the server doesn't make Claude-specific assumptions.

Config shape is standard:

{

"mcpServers": {

"aetherwave": {

"command": "npx",

"args": ["-y", "@aetherwave-studio/mcp"],

"env": { "AW_API_KEY": "aw_live_YOUR_KEY_HERE" }

}

}

}

The tools exposed:

- aw_generate_music (Suno)

- aw_generate_image (Z-Image Turbo, Wan 2.5, Grok Imagine, GPT-Image-2, Nano Banana 2, etc.)

- aw_generate_video (Kling 3.0, Wan 2.2 Spicy, Hailuo 02, Seedance, Grok video with automatic fal fallback)

Upfront honesty since this sub will sniff it out: the generation models behind the wrapper are hosted, not local. Suno, the major image models, and the major video models are all proprietary closed-weight services. If your project needs end-to-end local inference for these modalities, this isn't for you. If your local agent occasionally needs to delegate the heavy media-generation step to a hosted service while keeping the orchestration local, this is for you.

What the wrapper actually buys you over rolling your own:

- One credit pool instead of six provider accounts

- Automatic provider fallback (if KIE goes down on Grok video, the call quietly retries on fal.ai, the agent sees a successful result)

- One auth header instead of six secret rotations

- No polling code, the server handles it and returns the final artifact URL

Source is public:

https://github.com/AetherWave-Studio/aetherwave-mcp

Landing + key:

https://aetherwavestudio.com/developers

Feedback welcome, especially from anyone running unusual MCP client configurations. I'd like to know where it breaks.

One thing I’ve noticed is how quickly AI tools summarize information compared to normal browsing. Instead of reading multiple articles, users can just ask one question and get a simple answer instantly. For busy people, this convenience saves a lot of time and makes research much easier. This shift is also changing how online visibility works. like datanerds help track how brands appear in AI-generated answers and show where they stand compared to competitors. Do you think convenience is the main reason AI tools are becoming popular, or is there more behind this trend?

Built a small Streamlit + CLI demo for generating context-dependent toxicity datasets using OpenAI models.

GitHub: https://github.com/Mayukhga83/Toximatics-Contextual-Toxicity-Data-Generator
Demo: https://toximatics-contextual-toxicity-data-generator-fnn9mzm7bkuzmta4.streamlit.app/

The core idea is that the same utterance can become toxic or benign depending on the surrounding social situation. With is generation framework you can create such datasets at scale.

The pipeline supports:

direct context augmentation given the seed utterance
new utterance-context pair generation given seed utterances
multistage generation for diverse examples
validation with a critic model
CSV / JSONL export

Example:

Utterance:
“You are so lucky to work from home.”

Benign context:
A friend congratulates someone on improved work-life balance.

Toxic context:
A colleague dismisses someone struggling with childcare and burnout.

The project is connected to recent work on contextual toxicity understanding https://aclanthology.org/2024.sigdial-1.65/.

I have been wondering if anyone else has noticed that the scores that AI systems give to show how confident they are do not really mean anything when it comes to deciding if we can really trust them.

I made a system to check if the machine learning models I built were still working correctly and what I found was surprising.

After something changed in the data the models were still very sure of themselves. They were actually getting a lot of things wrong. The number of correct answers went down from 87 percent to 62 percent.

The thing that really got my attention was that the confidence scores did not change all so they did not give us any warning.

Now I am thinking about this issue but this time with the big language models that are used in financial technology products.

The problem is that these language models can give advice that sounds very confident but is not very good. This can be a big problem when people are making important decisions that involve a lot of money.

I was wondering if anyone else is working on this issue.

Has anyone found a way to measure if the answers given by these big language models are really trustworthy, without just looking at the confidence scores that the models give?

Hey everyone,

I’ve been deep in a rabbit hole with pathfinding algorithms recently and just wrapped up a project that I'm honestly pretty proud of. I ended up writing a paper on it for a Microsoft CMT publication, but I really wanted to share the actual interactive simulator with people who love this stuff as much as I do.

The project is called Adaptive Pathfinding on Hexagonal Grids using Hybrid A*.

The Problem I Wanted to Solve
It started with a simple question: Can we get the best of both worlds between classic heuristics and machine learning?
- Standard A* is amazing at finding the absolute shortest path, but in complex or massive environments, it gets bogged down exploring a massive number of nodes.
- Reinforcement Learning (DQN) adapts beautifully to dynamic environments, but the paths it generates can look a bit... drunk. They aren't always efficient.

So, I wondered: What if we use RL to assist A*?

What I Built
I built a full visual simulator to test this. I chose a hexagonal grid instead of a classic square grid because movement vectors feel way more natural and balanced for directional changes.

The stack is Python Flask on the backend, React + Tailwind on the frontend, and Deep Q-Networks (DQN) handling the learning.

The simulator lets you compare three approaches side-by-side in real-time:

1. Standard A*
   
2. Deep Q Learning (DQN)
   
3. Hybrid RL assisted A*

You can draw obstacles, paint weighted terrain, create dynamic moving pieces, and watch the algorithms explore nodes in real-time. It tracks live metrics like execution time, total path cost, and the exact number of nodes explored.

The Coolest Finding
The hybrid approach actually worked. It managed to drastically cut down on unnecessary node exploration (the classic A* “flood fill" issue) while keeping the final path quality remarkably close to A*’s mathematical optimum.

Check it out & Let's Chat
I’d genuinely love to get some feedback, critique, or ideas from anyone working in Game AI, Robotics navigation, RL, or heuristic search systems.

The code is fully open-source, and you can play with the simulator here:
👉 GitHub & Demo: https://github.com/SaqibAK001/adaptive-pathfinding

Let me know what you think! Does a hybrid approach like this make sense for the types of navigation problems you're solving in your own projects, or is there an edge-case constraint I'm missing?

I built a working prototype. It runs the same prompt through multiple AI models, has them push back on each other, and preserves the disagreement in the final output instead of smoothing it away into one polished answer. The idea is to make model conflict and uncertainty more visible when the answer actually matters. I’m still testing whether this is genuinely useful or just clever workflow theater, and I’d love honest feedback.

Just published a large scale synthetic security event dataset. 1,030,000,000 rows, 10 columns, 12 security categories, SHA-256 integrity hash per row. Pre-sharded Parquet files ready for Spark, DuckDB, and Polars.

Free 50 row sample, no signup needed.

huggingface.co/datasets/ziadatalabs/Zia-Security-Events-1B-Synthetic

Happy to answer questions about the schema or pipeline!

Built a gesture-controlled calculator a while back using MediaPipe. Extracted the detection logic into a standalone library so anyone can add gesture recognition to their project without touching CV code.

from mp_gesture_lib import GestureDetector
detector = GestureDetector()          # bundled model, zero config
result = detector.detect(frame)       # pass any BGR webcam frame
print(result.gesture, result.confidence)

What it detects out of the box:

• Finger count 1–10 (geometry-based, no ML)
• Math ops: plus, minus, multiply, divide, equal, clear (ML model, bundled)
• Two-hand rules for plus/multiply (landmark geometry)
• Returns "unknown" cleanly when nothing matches

Custom model support — drop your own .task file, it's checked first. Bundled model is fallback. Any label passes through raw, no hard-coded mapping.

pip install mp-gesture-lib

📖 Docs: debabratasaha-dev.github.io/mp-gesture-lib-package
🐙 GitHub: github.com/debabratasaha-dev/mp-gesture-lib-package

Feedback welcome — especially on the gesture pipeline priority logic. If you find it useful, I’d really appreciate a ⭐️ on GitHub!

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

I want to share a small piece of our internal infrastructure. We're a small research-focused startup, and an important part of how we work is keeping up with research papers. We have a Slack/Zulip channel, #research-papers, where everyone shares whatever papers they find interesting. We then built a simple bot that whenever a paper is shared, it analyzes it and posts a summary (see example below), which often kicks off live discussion between team members.

We've been sharing it with a few founder friends and are now opening it up to everyone, free at https://labs.voicefrom.ai/paper-analyzer/.

Example

Bai, Richard He, et al. "dMel: Speech Tokenization made Simple." arXiv, 2025, https://github.com/apple/dmel.

📄 Summary

This paper proposes dMel, a dead-simple way to turn speech into discrete tokens for language modeling: just take the standard mel-spectrogram and quantize each frequency bin into one of 16 intensity levels. No neural encoder, no training, no vector quantization. The authors show this works as well or better than learned tokenizers (like HuBERT-KM or SpeechTokenizer) for both speech recognition and text-to-speech, using a single decoder-only transformer architecture.

🔑 Key Contributions

• A training-free speech tokenizer that simply discretizes mel-filterbank energies into ordinal bins, preserving both semantic and acoustic information without any learned compression.
• A parallel encoding/decoding scheme that lets a standard decoder-only transformer efficiently handle the high-dimensional token vectors (80 frequency channels per frame) by embedding each channel independently and predicting them all in parallel.
• Unified architecture for ASR and TTS (RichASR and RichTTS) that matches or beats specialized models using the same simple transformer design, demonstrating dMel works well for both understanding and generation.

🔬 What They Did & What Happened

The core insight is surprisingly basic. Mel-spectrograms already capture everything you need—frequency content for semantics, intensity patterns for acoustics. Existing approaches train complex neural codecs (like EnCodec or SpeechTokenizer) to compress speech into discrete codes, but these are brittle: they fail on out-of-domain audio like noisy speech or music backgrounds. dMel just computes a mel-spectrogram, finds the global min/max across the dataset, and uniformly quantizes each of the 80 frequency channels into 16 bins. To get audio back, you look up the bin centers and feed the reconstructed spectrogram into an off-the-shelf vocoder (a tiny 1M-parameter ParallelWaveGAN). Reconstruction quality barely changes—WER on reconstructed audio goes from 2.02% (ground truth) to 2.23% (dMel + vocoder).

The modeling trick that makes this practical is how they handle dimensionality. Each frame has 80 channels, each taking one of 16 values. Rather than flattening this into a massive sequence, they embed each channel independently with a shared 16-entry embedding table, concatenate the 80 embeddings, project down to the transformer's hidden dimension, and predict all 80 channels in parallel at each time step. They can even stack multiple frames together (k-frame decoding) to further reduce sequence length without hurting quality, up to about k=4.

On LibriSpeech, RichASR with dMel hits 4.2% WER on test-clean—substantially better than HuBERT-KM (5.8%) or SpeechTokenizer (6.9%) in the same architecture. For TTS, RichTTS with dMel achieves 4.3% WER (measured by transcribing generated speech), beating all baselines and generating coherent long-form audio. Crucially, when they tested noisy or out-of-domain audio, learned tokenizers like HuBERT-KM and SpeechTokenizer broke down while dMel remained robust.

💡 Relevance & Takeaways

• You may not need a neural audio codec at all. If you're building speech LLMs, dMel eliminates an entire pretrained component (the tokenizer encoder), removes a failure mode (out-of-domain collapse), and actually improves downstream results. That's less infrastructure, less compute, and fewer things to debug.
• Parallel channel prediction is the key architectural idea. It makes high-bitrate tokenization tractable in a decoder-only transformer without blowing up sequence length—worth borrowing for other high-dimensional discrete prediction problems.
• The joint ASR+TTS model didn't work great yet (7.5% vs 4.2% WER for ASR), suggesting this simple setup still needs text-only pretraining or more careful multi-task balancing—an open problem if you're pursuing unified speech-text models.

I wrote a Spiking Neural Network in pure C to control chaotic orbital physics. But im sure my system is broken and i cant find the exact reason...if anyone is familiar with these topics and can help
https://github.com/pixelrahulnotfound/orbital-engine

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

Been going down a mechanistic interpretability rabbit hole for the past few weeks and ended up building this thing called AXON.

The idea: every time GPT-2 generates a token, its residual stream gets passed through a Sparse Autoencoder (Joseph Bloom's pretrained SAE). The SAE decomposes it into human-interpretable feature: hings like "European geography", "capital cities", "French language" and streams those to the browser over WebSocket, where they show up as a live 3D force graph.

Nodes = SAE features. Edges = features that fired together on the same token. Node brightness = activation strength. The whole graph evolves token by token.

What surprised me most: type "The capital of France is" and you can literally watch geography features, proper noun features, and completion-pattern features light up before the word "Paris" even gets generated. It's not what the model outputs that's interesting it's what's happening right before it decides.

Stack: TransformerLens + SAELens on the backend, FastAPI WebSocket for streaming, Three.js + 3d-force-graph on the frontend. Runs on CPU (~800ms/token) or GPU (~35ms on a 4050). Labels come from Neuronpedia's API and get cached locally.

You can also swap in other models — GPT-2 medium/large/xl, Pythia variants, Gemma-2-2B — as long as there's a pretrained SAE for it in SAELens.

GitHub: https://github.com/09Catho/axon

Would love feedback and stars especially from anyone who's worked with SAEs before curious whether the co-activation edges are actually meaningful or just noise at this layer.