Informatique🌐 EN
Raider
#games & comics#informatique
raider.io
📄 Full details →
👥 Target audience
Joueurs de jeux vidéo
🌍 Target countries
Monde
🗣️ Available languages
FREN
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When an AI coding assistant suggests a change, it usually describes the edit in words or a diff — but something still has to take that description and correctly apply it to your real file, matching up the right lines without breaking the rest of the code. Doing that with a big, slow general-purpose AI model is overkill and often unreliable. Morph built a small, specialized model whose only job is applying code edits, fast and accurately, so coding agents can act instead of just suggesting. Morph's flagship product, Fast Apply, is a 7-billion-parameter model that merges AI-generated code edits into existing files at roughly 10,500 tokens per second with around 98% accuracy, priced at $0.80 per million input tokens. It's available as an MCP tool that plugs directly into Claude Code, Cursor and other MCP-compatible coding environments, and is used in production by companies including JetBrains, Vercel and Webflow. Beyond Fast Apply, Morph offers a small suite of related infrastructure for coding agents: WarpGrep for agentic codebase search, FlashCompact for context compaction at 25,000+ tokens/second, and Reflexes for agent behavioral observability. Pricing includes a free tier (200 requests/month, 250,000 credits worth about $2.50), with usage-based pricing beyond that scaling across all four products.
| # | Tool | Best for | Price |
|---|---|---|---|
| 1 | Joueurs de jeux vidéo | — | |
| 2 | Developers and small-to-mid-size businesses seeking managed AI agent deployment without infrastructure management | — | |
| 3 | Enterprise and mid-market support teams using Freshdesk, Zendesk, Salesforce, or HubSpot; SaaS companies and e-commerce platforms managing high ticket volumes | — | |
| 4 | Développeurs et équipes utilisant des agents de codage IA ayant besoin de contexte factuel sur leur base de code | — | |
| 5 | Développeurs et utilisateurs individuels voulant un assistant IA local, sous leur contrôle total | — | |
| 6 | Chercheurs et ingénieurs en machine learning ayant besoin d'interpréter le comportement interne de leurs modèles | — | |
| 7 | Chercheurs biomédicaux, cliniciens et praticiens de la synthèse de preuves | — | |
| 8 | Équipes coordonnant plusieurs agents IA sur différents outils de collaboration | — | |
| 9 | Chercheurs et utilisateurs souhaitant comparer plusieurs modèles IA ou explorer des idées en branches | — | |
| 10 | Équipes construisant des systèmes multi-agents IA nécessitant traçabilité et persistance | — | |
| 11 | Développeurs et équipes DevOps ayant besoin d'agents IA tournant en autonomie (planifiés, en CI, ou en arrière-plan) | — | |
| 12 | Entreprises et PME qui veulent connecter leurs outils IA (Claude, ChatGPT, Copilot) à leurs bases de données ou systèmes internes sans embaucher de développeur dédié ; développeurs et analystes gérant plusieurs assistants IA. | — |
Managed hosting for persistent AI agent bots — isolated runtime, memory, missions, and integrations (GitHub, Slack, Sentry, MCP) so you can hire an AI worker instead of running your own agent infrastructure.
AI agent that resolves support tickets automatically on top of Zendesk, Freshdesk, Salesforce or HubSpot, building its own knowledge base and escalating tricky cases to humans, billed per resolution instead of per seat.
A knowledge-graph memory layer that gives AI coding agents persistent, factual context about your codebase, tickets, and docs — without another hosted service.
A local-first AI assistant that runs entirely on your machine, remembers context across apps and devices, and only acts with your explicit approval.
A local-first debugging tool that visualizes what's happening inside an AI model — attention, features, and agent steps — without cloud infrastructure.
An open-source agentic RAG system that searches 12 biomedical databases and produces a source-cited synthesis of the evidence.
An open-source, self-hostable platform for coordinating multiple AI agents across Slack, Discord, GitHub, and other tools in shared conversations.
A branching, canvas-based chat interface for comparing responses across Claude, GPT, Gemini, and other LLMs.
A local runtime that turns ad-hoc AI subagent delegation into durable, checkpointed, supervised workflows.
An open-source runtime for AI agents that keeps working unattended — on a schedule or in the background — on persistent cloud machines with spending controls.
Platform for building and hosting Model Context Protocol servers that connect AI tools to data sources without DevOps expertise.