This product was not featured by Product Hunt yet.
It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Product upvotes vs the next 3
Product comments vs the next 3
Product upvote speed vs the next 3
Product upvotes and comments
Product vs the next 3
GitHub
AI,LLM,Programing-langue,a2a
A typed, replayable, budget-aware programming language for LLM agents. Compiles to Python & TypeScript. - NekomyaDev/nudge
Top comment
What you get Typed LLM calls — output schema is a language type; violations trigger automatic repair, never reach your code Effect system — pure / LLM / Tool / IO effects inferred and shown in signatures Deterministic replay — full, hybrid, and live modes; traces are git-friendly JSONL Budget contracts — per-call, per-run, and per-repair USD ceilings (NUDGE_REPAIR_BUDGET) with static estimation (nudgec cost) Checkpointed agent state — crash, then nudge resume from the last checkpoint Native parallelism — par map, par race, par all with compile-time race safety Prompt Clippy — the compiler lints your llm""" blocks: vague instructions, missing output contracts, overlong prompts MCP & Python interop — consume real MCP servers over stdio as typed tools; escape to any pip package Real providers — one stdlib-only adapter for OpenAI / Gemini / Groq / MiMo / Mistral / Anthropic / Ollama; free tiers and local models work at $0 Trace viewer — nudgec trace-view opens a local web UI over any run: timeline, tokens, cost, repairs highlighted, par lanes color-coded (NTF v1.1 branch field) Trace diff — nudgec trace-diff a.jsonl b.jsonl answers "what changed when I edited the prompt?": totals and per-record deltas A2A agent-card export, LSP, OpenTelemetry — built in, not bolted on
About GitHub on Product Hunt
“AI,LLM,Programing-langue,a2a”
GitHub was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #104 on the daily leaderboard. A typed, replayable, budget-aware programming language for LLM agents. Compiles to Python & TypeScript. - NekomyaDev/nudge
On the analytics side, GitHub competes within GitHub — topics that collectively have 41.4k followers on Product Hunt. The dashboard above tracks how GitHub performed against the three products that launched closest to it on the same day.
Who hunted GitHub ?
GitHub was hunted by NEKOMYA. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of GitHub including community comment highlights and product details, visit the product overview.