Kameswara Sai Srikar MandaM.S. Data Science, Northeastern · graduating Dec 2026
I'm Srikar, an M.S. Data Science student and teaching assistant at Northeastern. At Launch Plan, until July 2026, I built for the people using a K-12 financial-literacy app through its pilot: a dashboard for school counselors, a college-fair kiosk for students, and LangGraph agents and a RAG search for the teaching team. Before that I led full-stack work on an AI-tool marketplace used by more than 10,000 people a month.
Open to forward-deployed engineer, data scientist and software engineer roles.
Each study asks one question, measures it on real data or real models, and keeps the results that came out wrong.
Most claims about how AI systems break are a screenshot of one bad answer. These put a number on one failure at a time, with the same rules each time: one factor at a time, a noise floor from a repeat run, and predictions written down before the full run.
The series: Measuring LLM SystemsBoth versions of the test are identical, so every winner is a false one. It should be 5%.
20,000 simulated tests at real headline-test sizes · how it was measured
A retrieval-poisoning study: plant one false or malicious passage among 10,000 that a question-answering system reads from, and measure who it reaches, how often three open models repeat it, whether it spreads, and what simple defences cost in accuracy.
How reliably do local LLMs use tools? Three models made the right first call about 80% of the time but finished only 36–69% of tasks: they stopped after one step of a chain and reported errors they could fix. More tools, vague names and loose schemas barely mattered.
How often does a harmless rewording change an LLM's answer? 300 maths, multiple-choice and extraction items on three local models, plus a 30B thinking model: 5–8% of answers move per rephrasing, shuffled options move the most, and the best-looking prompt doesn't hold up on new items.
Looks at a Git repository and says in plain English what state it's in and how to fix it: merge conflicts, a rebase stopped halfway, a rejected push, a 100 MB CSV about to be committed. A command-line tool and an MCP server for coding agents, on PyPI. It never changes the repository, and the tests prove it.
Getting around Boston from live public data: MBTA departures and service alerts, Bluebikes bike and dock counts, and door-to-door options by foot, bike or train, including one change between subway lines. A command-line tool and an MCP server, so coding agents answer with real numbers instead of guessing. On PyPI and the MCP registry.
Shows what a research paper is really standing on: the work it builds on, the work it argues with, and the assumptions it never defends. It reads all three straight from the paper's LaTeX, with no language model involved, and pins each one to the exact spot on the page. It covers 234 papers so far and publishes its own error rate: 80% precision on SciCite, 69% on ACL-ARC.
Re-analyses 22,743 real headline A/B tests from Upworthy and a 64,000-customer email test: how small real effects are, what checking early costs, how much winners shrink, and when variance reduction helps. The plan was locked before the confirmation data was opened, and every check replicated.
A racing game that runs in your browser, with nothing to install. Everything is made in code: the skies are shaders, the cars are modelled from cross-sections, and the engine, tyres, wind and music are synthesised live. 12 worlds, 14 cars and 4 modes, all built around drifting to charge your nitro. Works on phones, and offline.
A companion that lives in your git repository and grows with how you commit. Commits written by AI agents slowly possess it, and every form is a 3D model drawn as pixel art in the terminal. On PyPI, with 200+ downloads.
September 2026 - Present
January 2026 - July 2026
January 2026
Reads what a research paper stands on, straight from its LaTeX.
Python
A pixel-art companion that lives in your git repo. On PyPI.
Python
A read-only Git explainer, as a CLI and an MCP server. On PyPI.
Python
One bad passage in 10,000: how far a planted claim spreads through RAG.
Python
Questions about my work, a role, or a project idea. Send a message here or email me directly.
Srikarmks@gmail.com
