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u/PsychologicalDot7749

r/learnmachinelearning r/MachineLearning r/LLMDevs r/OpenSourceAI r/csharp
▲ 5 r/MachineLearning

TRACE: open-source hierarchical memory for LLM agents, 82.5% on MemoryAgentBench’s EventQA using gpt-oss-20B [P]

Built a memory system called TRACE that organizes agent conversation history into a topic tree (branches + summaries) instead of flat RAG chunks, and benchmarked it on MemoryAgentBench (ICLR 2026), specifically the EventQA accurate-retrieval task.

Its a pypi package:

pip install trace-memory

Results (F1):
• TRACE (gpt-oss-20B): 82.5%
• TRACE (gpt-oss-120B): 83.8%
• Mem0 (GPT-4o-mini, paper’s official number): 37.5%
• MemGPT/Letta (GPT-4o-mini, paper’s official number): 26.2%

Ran gpt-oss locally, so this is an open-weights model against MemGPT/Mem0 on GPT-4o-mini, not an apples-to-apples same-backbone test (I don’t have the money for open ai tokens).

I tried to get Mem0 running on gpt-oss-20B directly for fairness, but its fact-extraction step needs strict JSON output and gpt-oss’s responses didn’t parse cleanly (known issue, not gpt-oss specific. Same bug shows up with Gemini/Mistral too). Letta needs a full server setup so I skipped it.

Full JSON logs from both runs are in the repo if you want to dig into the methodology yourselves. GitHub: https://github.com/husain34/TRACE

reddit.com
u/PsychologicalDot7749 — 7 hours ago
▲ 8 r/OpenSourceAI+2 crossposts

TRACE: open-source hierarchical memory for LLM agents, 82.5% on MemoryAgentBench's EventQA using gpt-oss-20B

Built a memory system called TRACE that organizes agent conversation history into a topic tree (branches + summaries) instead of flat RAG chunks, and benchmarked it on MemoryAgentBench (ICLR 2026), specifically the EventQA accurate-retrieval task.

Its a pypi package:

pip install trace-memory

Results (F1):
• TRACE (gpt-oss-20B): 82.5%
• TRACE (gpt-oss-120B): 83.8%
• Mem0 (GPT-4o-mini, paper’s official number): 37.5%
• MemGPT/Letta (GPT-4o-mini, paper’s official number): 26.2%

Ran gpt-oss locally, so this is an open-weights model against MemGPT/Mem0 on GPT-4o-mini, not an apples-to-apples same-backbone test (I don't have the money for open ai tokens).

I tried to get Mem0 running on gpt-oss-20B directly for fairness, but its fact-extraction step needs strict JSON output and gpt-oss’s responses didn’t parse cleanly (known issue, not gpt-oss specific. Same bug shows up with Gemini/Mistral too). Letta needs a full server setup so I skipped it.

Full JSON logs from both runs are in the repo if you want to dig into the methodology yourselves.
GitHub: https://github.com/husain34/TRACE

u/PsychologicalDot7749 — 7 hours ago
▲ 1 r/learnmachinelearning

I built a self-reorganizing memory tree for PewDiePie's Odysseus Agent (Cuts token usage by ~30%, completely nullifies context bloat)

PewDiePie recently open-sourced his agent framework, Odysseus. The original memory retrieval system is surprisingly good out of the box, but it suffers from the classic local LLM problem: context bloat over long sessions.

To fix this, I ripped out the flat memory array and built TRACE: a self-reorganizing, hierarchical topic node tree.

Instead of just stuffing chunks into a prompt, TRACE dynamically constructs a conversation path.

  • Self-Reorganization: As the conversation flows, the B+Tree physically reorganizes itself. "Frozen" branches are summarized and tucked away, while active branches stay in context.
  • Negligible Context Bloat: If you are running rule-based agents and the rules change mid-conversation, context bloating becomes completely negligible because the old rule branches are frozen and summarized out of the active context window.
  • 30% Token Reduction: Because it only pulls surgical topic paths instead of massive sliding windows, it consistently consumes ~30% fewer tokens per turn.

It feels incredibly snappy for local models because it's actively pruning and reorganizing its own prompt context before the LLM even sees it.

Repo is here if you want to test the architecture:
🔗 https://github.com/husain34/odysseus-trace

Repo for standalone architecture:
🔗 https://github.com/husain34/TRACE

I'd love to hear how you guys handle dynamic rule changes in your own agents.

u/PsychologicalDot7749 — 1 month ago
▲ 32 r/csharp

I have no idea how to start with ASP.NET Core - where do I even begin?

I know C#, OOP, and SQL but every tutorial I open throws controllers, middleware, dependency injection, EF Core, and repository patterns at me all at once. I have no idea what order any of this is supposed to click in.

Do I need to understand all of it before building something? Or do I just start and figure it out?

Someone throw me a bone here

reddit.com
u/PsychologicalDot7749 — 2 months ago
▲ 0 r/csharp

Roast my C#/.NET backend roadmap - 2nd year CS student targeting banking internships

I'm a 2nd year Computer Engineering student in India and I've mapped out a roadmap to land a .NET backend internship by my 3rd year (mid-2027). I'd love brutal, honest feedback from people actually working in the field.

A bit of context: I've been doing Unity game development for about 2 years (3 game jam wins and a Unity Programmer Associate certification). So C# itself isn't new to me; I'm reasonably comfortable with OOP, classes, and writing real working code. What I'm switching into is the backend/enterprise side: ASP.NET Core, databases, cloud, and the patterns that actually show up in professional software. Thought that context might matter when you're judging whether my timeline is realistic.

My Goal: Backend developer role (ideally banking/finance domain) - open to India or GCC (Kuwait) market.

---

The Plan:

Phase 1 - Foundation (May to Sept 2026, ~4-5 months)
- Microsoft Back-End Developer Professional Certificate on Coursera (focusing on C#, ASP.NET Core, databases)
- FreeCodeCamp Foundational C# with Microsoft certification
- Azure Labs 1-4 simultaneously
- Build Project 1: a Loan Management & EMI Tracking API (JWT auth, role-based access, EF Core, Serilog, deployed on Azure)

Phase 2 - Security & Deployment (Oct to Dec 2026, ~3 months)
- Courses 5-8: security, scalability, DSA, DevOps
- Azure Labs 5-7 (Microsoft Entra, Azure DevOps pipelines, Docker/Containers)
- Build Project 2: an Internal Transaction & Invoice Management API (Unit of Work, background services, API versioning, rate limiting)

Phase 3 - Certification Sprint (Jan to Feb 2027, ~6 weeks)
AZ-900 (Microsoft Azure Fundamentals)
- Claim the Coursera Microsoft Back-End Developer certificate
- Rewrite resume with 2 deployed projects + 2 certs

Phase 4 - Internship Hunt (Mar to May 2027)
- Target .NET backend internships at Indian companies (Persistent, Mphasis, Pune startups)
- Leverage family network in Kuwait for GCC IT referrals
- Apply to remote Kuwait-based roles on LinkedIn and Bayt.com

---

My specific questions:

  1. Are these two projects (LoanFlow + CoreLedger Lite) actually impressive to a hiring manager, or do they look like tutorial projects? What would make them stand out?

  2. Is AZ-900 worth the time for an internship hunt, or should I skip it and spend that time on more projects?

  3. Is the Coursera Microsoft Back-End Developer certificate respected, or is it just a piece of paper?

  4. For the Indian .NET job market specifically - is there anything critical I'm missing (e.g., microservices, specific tools, DSA prep for interviews)?

  5. Is 8-10 hrs/week on this alongside academics realistic, or am I kidding myself?

Any honest feedback appreciated - especially from people who've hired interns or are working at banks/fintech. Thanks.

reddit.com
u/PsychologicalDot7749 — 2 months ago