Tagged llm

Build an Android app that runs an open text LLM on device with LiteRT-LM

Split extracted text into overlapping chunks sized for LLM context windows

Pick the output format for an LLM, parser or archive from extracted documents

Build a persistent local wiki from PDFs, Markdown and text files you can query and lint

Test LLM apps and agents for prompt injection, tool abuse, RAG poisoning and MCP risks

Build MCP servers that expose external APIs as tools an LLM can call

Route LLM requests across providers with failover and cost tracking through Vercel

Store every LLM generation with unique IDs, addressable URLs and cost tracking

Connect third-party LLM agent runtimes to Twilio voice, messaging and memory

Build a compressed symbol map of a TypeScript or Swift repo for LLM context

Apply behavioral guidelines that cut common LLM mistakes in code changes and fixes

Write production GenLayer contracts with pinned runner versions and LLM resilience

Write Reward Kit verifiers with LLM judges that score task output

Test LLM apps for direct and indirect prompt injection and prompt leaks

Test LLM guardrails against translation, encoding and fictional-framing evasion

Test LLM system prompts against override, constraint negation and role reversal

Test multi-turn memory poisoning and cross-session context leaks in LLM apps

Test LLM APIs for rate-limit abuse, cost amplification and schema bypass

Assess LLM training and feedback pipelines for data and reward poisoning

Test LLM outputs for covert channels, schema breaks and data exfiltration

Test LLM deployments for misinformation, impersonation and hallucination abuse

Test multimodal LLM inputs for hidden text and image or audio injection

Test LLM apps for system prompt, training data and key extraction risks

Test LLM services for denial of service, quota exhaustion and billing abuse

Hunt LLM training data and model poisoning in fine-tuning and feedback loops

Hunt LLM excessive agency where agents hold too many tools or permissions

Hunt LLM output handling flaws that let model text drive XSS or code execution

Route LLM red team tests across the AATMF v3 tactic catalog

Hunt LLM overreliance where wrong outputs feed automated decisions

Hunt LLM leaks of PII, secrets and other-tenant data through model outputs

Hunt LLM supply chain risks in models, datasets, plugins and MCP servers

Hunt LLM system prompt leaks that expose rules, secrets and tool lists

Hunt LLM denial-of-wallet and resource exhaustion through unbounded requests

Crawl a whole website into LLM-ready markdown pages with metadata

Grade staged git changes with an LLM judge before you commit them

Route AI, LLM, agent, RAG and prompt questions to the right guides

Hunt prompt injection and data exfiltration bugs in LLM-powered features

Call LLMs from code snippets, compare models and run batch inference

Design and evaluate agentic harnesses for LLM tools with approvals and memory

Save, version and update LLM-generated widgets, HTML pages and markdown outputs

Convene a council of models from different vendors to vote on hard questions

Connect the Oracle database MCP server to a Python LLM agent as LangChain tools

Run a multi-LLM council with personas, budget caps and a synthesized verdict

Fine-tune LLMs on Google Colab GPUs with Unsloth via a remote bridge

Generate and test hypotheses on tabular data with an LLM-driven loop

Evaluate LLM outputs with judge models, rubrics and pairwise comparisons

Validate SMILES strings and catch invalid molecules from LLM output

Fine-tune LLMs with supervised, RL and LoRA training on Tinker cloud

Fine-tune LLMs with LoRA, QLoRA and GRPO in Unsloth and export GGUF files