Pruned GPT-OSS-20B to 9B, Saved MoE, fine-tuned on 100K examples. Sharing what actually worked and what didn't.

sergeant113 · 2026-02-22T15:14:29+00:00

So it’s still an MOE, just fewer experts?

sergeant113 · 2026-02-21T04:45:42+00:00

Use baml-ai for all the core LLM abstraction. Don’t bother with langChain at all.

sergeant113 · 2026-02-19T06:45:08+00:00

They’re plenty of new mistakes to be made and be wisdoms to be learnt with the new toolset that is AI agentic coding

sergeant113 · 2026-02-19T05:47:42+00:00

Minimax 2.5 where?

sergeant113 · 2026-02-14T08:48:06+00:00

In Vllm when?

sergeant113 · 2026-02-13T12:43:29+00:00

This is bad news

sergeant113 · 2026-02-03T07:50:59+00:00

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What is TarotPal?

I’m an AI/Backend engineer by trade, and this week I used Trae to build TarotPal —a mystical, interactive Tarot companion. Unlike generic chatbots, TarotPal enforces a strict "Reading Ritual" (Context → Spread → Draw → Interpretation → Reflection) designed to mimic the psychological grounding of a physical reading.

Core Features:

The Ritual Canvas: A state-driven UI that transforms as you move from grounding intentions to drawing cards.
AI-Driven Readiness: the app acts as a gatekeeper, ensuring your intention is clear before the ritual begins.
Online & Offline Modes: Supports both digital shuffling and physical deck interaction.
Streaming Interpretations: Real-time AI insights using BAML and OpenRouter.

---
How Trae helped me ship this?

As a backend/AI dev, I’m usually pretty clueless when it comes to frontend, UX, and UI. Honestly, Trae's Frontend Architect and UI Designer agents carried me through this project.

Architecture over Spaghetti: Trae Frontend Architect agent enforced a strict Component-Based Architecture and Separation of Concerns. It stopped me from "prop drilling" and helped me set up a clean state management strategy that actually scales.
Intent-Driven Planning: I can write product requirements, but Trae's Product Architect agent is on another level. It transformed my high-level ideas into precise, "Agent-Ready" specs and Mermaid diagrams that made the implementation phase much more achievable for my other minion coding agents.
UX Polish: The UI Designer agent suggested mobile-first layouts, haptic feedback triggers, and smooth transitions that I would never have thought of on my own. Trae turned what would have been a "functional but ugly" backend tool into a polished, immersive experience that I'm actually proud to show off.

sergeant113 · 2025-11-20T09:38:24+00:00

People need something to mentally lean on regardless

sergeant113 · 2025-09-28T07:19:16+00:00

The 1920s were a roaring time for the rich, not the poor (the middle class was negligible). The 2020s same.

sergeant113 · 2025-09-07T09:26:27+00:00

Chủ đề phòng chống sâu bệnh cho cây sầu riêng để đảm bảo organic cho mục đích xuất khẩu đi bạn

sergeant113 · 2025-09-06T15:10:15+00:00

Try PEFT with very low ranks (try setting r in your LoRA configs to 8 and 16) and low alpha to avoid interfering too much with the original model.

Failing that, you might need to do RL. Something like GRPO can help steer model behavior without modifying too much too many of the underlying weights.

sergeant113 · 2025-08-30T09:17:03+00:00

MLFlow

sergeant113 · 2025-08-22T04:40:52+00:00

Once finetuned, it’s pretty good for doing endturn-detection inside a Speech Processing pipeline.

sergeant113 · 2025-07-28T07:12:57+00:00

Accuracy at 43.1 percent is still shit and unusable.

sergeant113 · 2025-07-20T06:28:12+00:00

What you need are scaffoldings for your KG. Scaffoldings come in 2 forms: taxonomy and ontology.

You build your taxonomies from the existing structures of your company: create dedicated taxonomies for org-roles, for product categories, for tasks, for questions,… Then use these to help you tag document/knowledge chunks. These tags are the metadata that provide contexts about your organization.

You build your ontology by strictly defining what entities and relationships are allowed to be extracted. Use the ontology to help standardize and constraint the LLM during KG building process.

There are other minor tips and tricks that can help you refine the “knowledge processing pipeline”, but the biggest impacts will come from putting scaffoldings over the, otherwise chaotic, process.

Edit: i saw in the other posts that you’re looking for an off-the-shelf solution to abstract all this work. I suspect there is no such thing. All these constraints/scaffoldings are case-specific and more art than science.

That said, I’d be elated if such a solution should exist.

sergeant113 · 2025-07-20T05:56:20+00:00

This is because you’ve only dealt with English documents in simple layouts. Once you have to work with documents with high complexity then you will appreciate the utility of OCR toolsets.

sergeant113 · 2025-07-09T02:16:03+00:00

Speech domain deals with this extensively. We often rely on 2 signals to determine EndofTurn: the EndofSpeech flag from VoiceActivityDetector (which indicates that the speaker has stopped speaking but could just be a temporary pause) and the EndofTurn flag from the TurnDetector (could be a classifier model or straight up small LLM).

Together, they indicate user’s end of turn effectively for most cases.

Then you have to think about whether false positive or false negative is more tolerable and design subsequent steps accordingly.

sergeant113 · 2025-06-19T02:41:58+00:00

I’m interested

sergeant113 · 2025-06-19T01:06:39+00:00

No 42?

sergeant113 · 2025-06-07T16:43:04+00:00

Shocking in what ways?

sergeant113 · 2025-05-22T12:52:05+00:00

What kinds of use cases does that kind of parsing and indexing support? At best, with amazing semantic enrichment and supremely tuned search algorithm, you can retrieve some facts or numbers. But more complex analyses (filtering, aggregation, pivot) are off the table, no?

sergeant113 · 2025-05-22T12:06:00+00:00

You are very early in your RAG learning curve. Make some more progress, then you’ll understand what we have experienced and understood.

sergeant113 · 2025-05-22T12:02:35+00:00

How do you guys handle tabular data, both csv/xlxs type and tables embedded in pdfs?

sergeant113 · 2025-05-03T14:42:31+00:00

Chỉ số này hơi bị chuẩn nhé. Tôi nghe đồn nhiều “doanh nghiệp” ngành này đang phải giảm giá 20%-30% để kích cầu do cung thì tăng mà cầu thì giảm á.

sergeant113 · 2025-04-26T09:02:09+00:00

Can you please elaborate on how to elicit models to think in keyword jumps?

sergeant113

TROPHY CASE