Opportunities

Front-end development accumulates change after change at the repository level, weaving complex cross-file dependencies that current LLM coding agents tuned for

Financial observations are continuous, heterogeneous, and noisy, whereas decoder-only next-token models are usually built around discrete symbolic inputs. We in

Randomised testing is a widely-used approach to software validation, yet its theoretical foundations remain thin. In particular, the fundamental question of wha

The rapid deployment of machine learning systems across cloud, edge, and enterprise environments has brought model optimization to the forefront of systems-engi

Safety claims on self-improving agent runtimes are almost always self-graded: a policy file, a guardrail, or a README commitment. We describe falsifiable releas

Production LLM-agent frameworks expose control primitives -- human-in-the-loop approval gates, run cancellation, and execution timeouts -- whose names and docum

Show HN: ContextVault – Shared memory layer for your AI and your team

Show HN: Tilion – Stealth Browser Infrastructure for Agents

Research-backed agent skills and tools for premium image, video, audio, voice, and generative media production across AI coding assistants.

Self-hosted AI meeting intelligence — transcription (faster-whisper), speaker diarization (pyannote), and LLM summaries on infrastructure you control. An open-s

A local-first second brain for scientists, researchers, coders, and nerds.

Persistent, local, cross-IDE memory for AI agents — markdown source of truth, LanceDB-powered semantic search, zero cloud dependency

Show HN: Nobie – an Excel-compatible runtime for agents and humans

Launch HN: Agnost AI (YC S26) – Extract user feedback from agent conversations

Languages with rich static semantics, such as Rust, provide stronger guarantees for AI-generated code, but their strictness makes generation more difficult. Off

Launch HN: Coasty (YC S26) – An API for computer-use agents

Large Language Models (LLMs) have revolutionized AI services, but a critical tension emerges: while personalization improves model performance, it consumes scar

LLM-based coding agents repeat the same classes of mistakes across sessions because they lack a mechanism to retain corrections from human review feedback. We p

Large language models (LLMs) have improved automated program repair (APR), but two limitations remain. First, raw execution traces are often too large and repet

Quantum technologies are maturing into systems that classical engineering must build, verify and maintain. The model-driven community has begun to respond with