u/naruto_uzumaki00

if you build on the LLM APIs, a big chunk of every request is tokens the model doesn't need - resent system prompts + history, whole files dumped into context, easy calls routed to the frontier model. i built a vendor-neutral layer that strips that, and measured it on the providers' own billed tokens (heavy tasks):

gpt-5.5: 16,875 -> 2,232 input tokens (86.8% fewer), quality 3/3 -> 3/3

opus 4.8: 26,573 -> 3,343 (87.4% fewer), 3/3 -> 3/3

two ways to drop it in:

- OpenAI/Anthropic-compatible proxy - point base_url at it, keep your key. every request gets the levers applied + an X-TRL-Tokens-Saved header.

- MCP plugin for Claude Code / Codex - the agent gets retrieve_code(query) / explain_symbol(name) and pulls only the relevant AST slices instead of dumping whole files. since Claude Code and Codex bill by tokens, that stretches your weekly cap.

four levers under the hood: prefix caching, tail compression with a deterministic guard that re-injects any number the compressor drops, AST/text retrieval, and cascading verifiable steps to a local model.

honest negatives, in the repo: static embeddings didn't beat plain keyword retrieval in my eval; a real 3B compressor dropped ~1/3 of load-bearing numbers before i added the guard; suites are small + favorable. Apache-2.0, free, reproducible benchmark included (validate/heavy_bench.py). repo: https://github.com/AryanGonsalves/trl-token-reduction - would love people to break it.

Hi all — I'm a Master's student (Data Science, graduating May 2026) who has been actively applying for full-time Data Scientist / Data Analyst / BI roles in the US for a few months with no callbacks, and I'd really appreciate a critical look at my resume.

Background: MS in Data Science, Analytics & Engineering (GPA 3.8), and before that a B.Eng in Computer Engineering with an AI/ML honors track. I've built several end-to-end ML projects (a fraud-triage platform, a time-series forecasting system, an LLM-powered agent) and had one internship as a game developer.

Targeting: Data Scientist, Data Analyst, and BI / Decision Analytics roles, entry-level / new-grad.

Location: Based in Arizona, open to relocating anywhere in the US, open to remote.

Visa: F-1 OPT (STEM-eligible), so no employer sponsorship is required — not sure if that's still causing some ATS/recruiter filtering.

Challenge: very few interview callbacks despite what I think is solid, quantified project work. Not sure if it's the resume structure, how I'm presenting technical depth, or something else entirely.

Would love feedback on: whether the projects section reads as too dense/technical, whether the summary at the top is doing its job, and anything that would make a recruiter skim past me. Thanks in advance!

Hey all — I made this and wanted to share it for feedback (I'm the author, so flagging this as a showcase/self-promo post).

The problem I kept hitting: when an agent calls a broken or drifted MCP tool, the model often doesn't error out — it improvises around the bad response and keeps going, quietly contaminating downstream steps. No crash, no alert.

PolyForge scores every MCP server your agent depends on on a 9-point rubric (commit recency, sole-maintainer risk, CI status, unpatched CVEs, clean install, hosted uptime, schema stability), buckets them production / light / dead, and can fail CI if a dependency is dead. It can auto-gather the cheap signals (last commit, contributors, CI) straight from a repo on GitHub, and there's a fallback router that resolves a capability to the healthiest server.

Honest about where it's at: early, from-source only (PyPI planned), and routing picks the healthiest server but doesn't execute MCP calls yet. The rubric weights are a sensible default, not validated against a big labeled set — which is exactly the feedback I'm after. If you run agents on a stack of MCP servers, does the scoring match your gut, and what signals would you add?

Repo: https://github.com/AryanGonsalves/polyforge (MIT). Tear it apart — issues/PRs welcome.