Run the primary agent loop on fast general models, served on custom kernels through one OpenAI-compatible API.
Offload the sub-tasks that general models do slower and pricier to models trained end-to-end for one job.
Fast general models for the primary agent loop — chat, code generation, and reasoning. Qwen 3.5 397B, MiniMax M2.7, and more through one OpenAI-compatible API.
Implement a Redis cache middleware for Express with TTL support and cache invalidation.
#1 on SWE-Bench Pro. WarpGrep searches the repo in a separate context and returns the right files in under 6 seconds. 15.8% cheaper, 22% faster than in-context search.
Fix the auth bug in the login flow
I need to find where JWT tokens are validated...
Fast Apply merges model-generated edits into files at 10,500+ tokens per second. No re-reads, no broken search-and-replace.
Compact compresses long sessions 50-70% at 33,000 tok/sec without losing signal. Byte-identical, not summarization. +0.6% on SWE-Bench Pro.
Open-weight frontier models, codegen-optimized inference
Output Speed (tok/s)
Purpose-built models for coding agent tasks
MCP routes your coding agent through Morph APIs. Try Morph in minutes, then integrate directly through the API or SDK for production.
Deploy Morph on your own infra - on-prem or cloud.
Flexible, high-capacity rate limits.
99.9% uptime SLA with top-tier support .
Ready-to-sign agreements for enterprise compliance.
Anthropic's multi-agent system outperformed single-agent Opus by 90%. The reason isn't better models. It's that intelligence degrades when you ask one agent to do everything, and improves when you let specialists work in isolation.
Lovable and Cursor are obvious coding agents. What's less obvious: Linear, Posthog, customer support agents, marketing agents, and hardware telemetry platforms are all becoming coding agents too. Code is the substrate that lets you build the feature that builds all features.
Rich Sutton's bitter lesson predicted that scaling compute beats hand-engineering. Seven years later, coding agents are proving him right. The fix isn't smarter models. It's specialized infrastructure.
After studying every major coding agent harness, one pattern dominates: context pollution determines task failure. Search wastes 60% of agent time. Edit retries inject redundant file reads. The agents that win are the ones that keep their context clean.
Flash Compact drops 50-70% of an agent's context at 33,000+ tokens/second while keeping every surviving line verbatim. Two modes: objective compaction strips filler with no guidance, query-based compaction weights keep/drop decisions against what the agent needs next.
AWS selected Morph as a featured case study — covering the infrastructure behind 10,000 tokens/sec, how Binance achieved 50-70% productivity gains, and why we built on EC2 P5 and SageMaker.
Critical takes on the latest in codegen.
Scale AI's benchmark for coding agents: 1,865 tasks across 41 repos. Leaderboard, scores, and why WarpGrep v2 lifts every model to #1.
Real data on when Codex destroys Claude Code and when it doesn't. Token economics, failure modes, and which $20/month actually delivers.
Every serious Cursor alternative benchmarked: Claude Code, Windsurf, Cline, Copilot, Aider, Codex, and OpenCode.
Claude Opus 4.5 leads SWE-bench at 80.9%. Grok 4 hits 81%. Scores, API pricing, speed, and why the harness matters more than the model.
How coding agents actually work, what separates harnesses from models, and where the field is headed.
Set up Playwright MCP in Claude Code, Cursor, or Codex. MCP vs CLI token costs and Stagehand comparison.
Native install, Homebrew, npm. Auth, CLAUDE.md, MCP setup, and troubleshooting.
Why LLMs degrade as context grows. 30%+ performance drop from lost-in-the-middle, and how subagent isolation reduces context rot by 70%.
The difference between a prompt and an agent that works. How to structure context so coding agents stay coherent across long sessions.
Technical analysis of AI coding agent harness architectures. Go-based OpenCode (75+ providers) vs Rust-based Codex (GPT-5).
Every head-to-head comparison in one place. Cursor, Claude Code, Copilot, Windsurf, Codex, Aider, Cline, and more.
Search-replace blocks with git merge syntax: limitations, accuracy issues, and why semantic editing achieves 98% vs 70% success rates.
Browserbase MCP gives coding agents hosted browser sessions, MCP tools, and a cleaner path from local browser loops to production browser infrastructure.
Where Stagehand fits next to Browserbase MCP, Playwright MCP, and Browser Use. Framework primitives, reliability, and when to use it.
Browserless API supports REST endpoints for task-shaped browser jobs and CDP WebSockets for Playwright or Puppeteer. Setup, tradeoffs, and self-hosting.
Run Browserless in your own environment with Docker, queue browser workloads, and expose Playwright/Puppeteer-compatible endpoints with better operational controls.
Set up Claude Code with LiteLLM as a unified endpoint. Learn when LiteLLM helps, how the proxy works, and the practical tradeoffs.
Claude is the broader Anthropic assistant stack. GitHub Copilot is the broader GitHub coding stack. Pricing, product scope, and workflow fit compared.
Kiro pricing is simple on paper: plans, credits, and overage. This guide breaks down the actual credit math, trial rules, and tradeoffs.
An LLM router classifies prompt difficulty in ~430ms and routes to the right model tier. 40-70% API cost savings with under 2% quality loss on hard tasks.
Claude Sonnet 4.5 costs 3.75x more than Haiku 4.5. Pricing, speed, quality tradeoffs, and how automatic model routing cuts costs 40-60%.
What an LLM gateway does, how it differs from a proxy and a router, key capabilities, open source options, and where intelligent routing fits in.
OpenRouter and LiteLLM solve provider access. Neither solves model selection. Comparison of routing logic, cost optimization, latency, and how intelligent routing fills the gap.
A practical guide to reducing LLM API costs. Five levers: model routing (40-70% savings), context compaction (50-70% token reduction), prompt optimization, caching (90% on cache hits), and batching (50% discount).
An AI infrastructure company writing honestly about the AI bubble. Bain found 10-15% productivity gains, METR showed devs 19% slower with AI, CodeRabbit measured 1.7x more bugs. What the data says, and what survives the correction.
What AI coding actually costs: token waste, agent loops, context bloat, subscription stacking. Real pricing data for Claude, GPT-5, Gemini, and how to cut spend 40-70%.
Real AI hallucination examples across legal, medical, and coding domains. Measured hallucination rates from 0.7% to 29.9%, why token prediction makes hallucination inevitable, and architectural strategies that reduce it.
An honest Claude vs ChatGPT comparison from a team that routes production traffic to both. Pricing, benchmarks, strengths, weaknesses, and why model routing beats picking one.
AI washing is adding 'AI-powered' to a product that uses no AI, or wrapping a ChatGPT API call and calling it proprietary technology. Documented SEC and FTC enforcement actions, the API wrapper problem, and a checklist for evaluating whether a vendor's AI claims are real.
An AI infrastructure company's honest assessment. METR found AI made experienced devs 19% slower. Bain measured 10-15% gains. CodeRabbit found 1.7x more bugs. Junior dev postings dropped 60%. The job is changing, not disappearing.
