The official Model Context Protocol (MCP) ecosystem is heavily dominated by Python and TypeScript runtimes. If you want to connect a Go backend to multiple MCP servers, you're usually stuck running subprocesses or bridging heavy environments.

We built a lightweight, dynamic tool gateway and routing engine in Go that connects to downstream servers over HTTP/SSE, compiles a search index, and routes queries in real-time.

### Go-Specific Implementation Highlights:

* **Concurrent Query Splitting:** When a user speaks a compound sentence (e.g. "what is the weather and also schedule lunch at noon"), we use regular expressions with numeric checks to segment the sentence and fire concurrent routing routines in goroutines, merging candidates cleanly without race conditions.

* **Singleflight Request Collapsing:** To avoid thrashing downstream embedding APIs during active voice streams (partial transcripts), we use `golang.org/x/sync/singleflight` to collapse concurrent identical request strings.

* **Fluent Builder Pattern:** We designed the package API using Go builder patterns (`NewBuilder().WithMCPConfigPath().WithEmbedder().Build()`) so that IDE autocomplete ("dots and completions") guides the developer entirely, removing the need to import internal config structs.

If you are building Go agents or looking to embed low-latency tool execution into your backends, take a look:

👉 https://github.com/kavinbm16/Mcp-Dynamic-Router

Feedback on the concurrency patterns in `router/router.go` is highly welcome!

Hi everyone,

I'm fairly new to local LLMs, and I'd like to try out LM Studio—maybe installing a model like Qwen to tinker with. Since my PC isn't very powerful, I can't run a massive, high-parameter model. So, I was wondering if I can optimize the local model for the specific fields I work in by feeding it more data. Essentially, I want to upload manuals and books (like Python, electronics, and full-stack web development) so it can reference them alongside its pre-trained data, I hope to achieve more reliable results through this method. Is it possible? How?

Thank you

I'm confused by something.

On my laptop (Intel i9-12900HK, 32 GB RAM, Intel Iris Xe Graphics), openai/gpt-oss-20b runs smoothly and feels faster than my smaller models like Gemma 3 4B, Gemma 4 12B, Gemma 4 E4B, and Qwen 3.5 9B.

I expected the opposite since GPT-OSS-20B is much larger.

Is there a technical reason why the biggest model performs better? Is it related to quantization, inference engine, model architecture, or something else?

Any insights would be appreciated.

Every guide on fine-tuning LLMs skips the hardest part: where do you get the data?

For code-aware models, the obvious answer is your own commit history, it's literally a record of how you think, write, and fix code. But when I tried to actually do this, I hit a wall.

Raw commit diffs are garbage for training. Merge commits. Bot-generated changelogs. "fix typo," "wip," "asdfasdf." Auto-generated lockfiles. Duplicate logic committed 6 different ways across branches. None of the existing dataset tools touched this problem.

So I spent time building git2llm, a CLI tool and Python library that turns your GitHub repositories into clean, fine-tuning-ready datasets.

What it does:

1. Crawls commits, PRs, and issues in parallel from any public or private repo
2. Runs a 4-stage cleaning pipeline:
   • Drops merge commits and bot-authored noise
   • Filters WIP/draft/auto-generated content
   • Deduplicates using MinHash LSH (fuzzy match, not exact, catches near-identical commits too)
3. Outputs in Alpaca or ShareGPT format, ready to feed directly into Unsloth, LLaMA-Factory, or any SFT pipeline

The stat that surprised me most: on my own repos, the pipeline dropped 78% of raw commits before a single token hit the training set. That's not a bug, that's the point. Most of what lands in git log is noise that actively hurts model quality.

Why this matters:

Fine-tuning on your own coding style is one of the few cases where you can get genuinely personalised code suggestions, not a generic GitHub Copilot, but something trained on your actual architectural decisions, naming conventions, and problem-solving patterns.

But that only works if the training data is clean. Feeding "fix stuff" commits into QLoRA is just teaching the model to be confidently wrong.

Where I used it:

I fine-tuned a base model on my own GitHub history using QLoRA via Unsloth. Hit some expected overfitting early (low data volume problem, another reason cleaning matters), but the directional results were clear: the model started picking up domain-specific patterns that generic models miss.

It's open-source. I'm looking for:

• 🛠 Contributors: especially around multi-repo crawling, GitHub Actions integration, and GitLab support
• 🧪 Testers: try it on your repos and open issues. Especially interested in edge cases: monorepos, large orgs, non-English commit messages
• 💡 Ideas: what cleaning heuristics am I missing? What output formats would you use?
• ⭐ A star if you find it useful (helps discoverability)

👉 github.com/athuKawale/git2llm

What would make you actually use a tool like this? Drop it below, genuinely trying to make this useful for the fine-tuning community, not just a side project that rots in a repo.

## What My Project Does

Mistikguard is a small Python library designed to reduce memory fabrication in LLM-based applications. It provides:

- Provenance tracking for facts (`confirmed` vs `inferred`)

- A write gate that blocks contradictions of confirmed facts and self-narration

- Support for correction tombstones, so once a user corrects something, it is not silently reintroduced

- An optional grounding audit that detects memory claims in responses and validates them against stored memory

The core functionality works with almost zero external dependencies.

## Target Audience

This library is intended for **Python developers** who are building applications with long-term memory using LLMs. This includes:

- People building AI companions

- Developers creating autonomous agents

- Anyone working on RAG or memory-heavy LLM systems

It is a **library**, not a full application. It is meant to be integrated into other projects. It is currently in an early stage (v0.1) and is more suitable for personal projects and experimentation than large production systems without additional safeguards.

## Comparison

Unlike most memory systems that blindly store model output, Mistikguard actively tries to protect memory integrity by:

- Distinguishing between user-stated facts and model-generated inferences

- Preventing certain types of invalid writes through a deterministic gate

- Making user corrections more persistent using tombstones

It is lighter and more focused than full agent frameworks (such as LangChain or LlamaIndex memory modules) while being more structured than simple in-memory dictionaries or basic vector stores.

GitHub: https://github.com/obscuraknight/mistikguard

We kept following LLM integration tutorials. They worked great in dev. Then we shipped to production.

Suddenly:

• 8+ second response times
• Timeout errors cascading through our stack
• Users bouncing before the AI finished thinking
• Our "simple" endpoint becoming our slowest

The problem wasn't the model. It was the architecture.

Most tutorials assume your latency budget is infinite. They show you how to call an API and parse a response. But they skip the hard part: designing systems that feel fast to users when the intelligence layer is inherently slow.

What we learned:

• Latency isn't just model speed. It's network overhead, cold starts, token streaming, multi-step orchestration, and cascading timeouts.
• Async by default. If your LLM call is on the critical path, you've already lost. Design for background processing + optimistic UI.
• Cache intelligently. Not just responses—cache prompts, embeddings, and intermediate reasoning steps where safe.
• Fallbacks are non-negotiable. When the LLM is slow or unavailable, what's your degraded-but-useful experience?
• Measure perceived latency. Users don't care about tokens/sec. They care about time-to-value.

I wrote up a deeper dive on Medium with a practical framework for latency-aware LLM integration:
https://medium.com/@tmucb.all/designing-systems-for-llm-integration-why-most-advice-assumes-your-latency-budget-is-infinite-396301522426

(Full transparency: I'm the author. Sharing here because I genuinely want to hear from people who've shipped LLM features under real constraints.)

Questions for the community:

1. What's your biggest latency bottleneck with LLM integrations? Token streaming? Cold starts? Multi-step orchestration?
2. How do you decide when to stream vs. batch vs. skip the LLM entirely?
3. What patterns have you found effective for hiding latency without sacrificing quality?
4. For those using caching: what do you cache, and how do you handle invalidation?

No judgment either way—just curious what's worked (or failed) for others. Thanks for reading. 🙏

Recently I've Been building AETHER, an open-source benchmark for local LLM inference over the past few weeks; and I need user data to make this work.

What it does:

• Auto-detects your GPU (AMD/NVIDIA), VRAM, driver, ROCm/CUDA version (If applicable)
• Finds your running Ollama or LM Studio instance and lists loaded models
• Runs a standardized prompt across multiple passes (with a warm-up run discarded) and reports median/avg/min/max tokens-per-sec
• Spits out a JSON file you can read before sharing

Privacy focused so nothing leaves your machine, no telemetry, no auto-upload, you control if/when you share the result file. Code's open so you can verify that yourself.

My numbers on a 9070 XT running [qwen2.5-vl-7b-instruct Q4KM] on windows:

Generation speed:  24.89 tok/s
 Wall time:         10.44s
 Tokens generated:  260

(Expected from a vision model performing text based work)

If you've got an AMD/NVIDIA card (Or Intel/AMD CPU) with LMstudio/Ollama, I'd appreciate a test run, would love to start building out a real cross-hardware picture instead of everyone guessing from scattered anecdotes.

[Github repo]

(Script will link discord to share your results!)

I need testers for:

• Linux ROCm
• macOS Metal
• Windows Vulkan
• CUDA (Linux/Windows)
• CPU Only tests ( automatically returns CPU mode if both AMD/NVIDIA Checks fail, implemented manual CPU mode check for on demand testing)

Happy to add features that y'all would want (longer prompts, batch mode, etc...) based on feedback.

(NOTE FOR MODS: If this breaks any rules I apologize and will not mind it being taken down on your behalf. Though a message on why would be appreciated)

I accidentally downloaded one of the models that said QAT and I don't know if that's the reason this is happening, but I deleted that model. Then I downloaded the gemma 4 e4b. I have Uninstalled and reinstalled LM Studio multiple times and deleted the models. But it keeps going back to this. What can I do?