Understand the project
Navigate files, symbols and language-server context. Find the code that matters before making a change.
Built in Ethiopia. Available worldwide.
With open-source models.
Turn ambitious ideas into precise work. Lamied brings AI-assisted development and native performance analysis together, so you can build with clarity and spot scaling bottlenecks before release.
Linux and Windows · Free editor. No account needed to download.
The execution approach
Better instructions make smaller models more useful. Our architecture is designed to reduce ambiguity, unnecessary context and repeated work.
Follow the request as Lamied breaks it down, creates tools, and saves results.
One request enters the Mother Model.
Planned execution architecture. Output is saved on completion or interruption; task records persist until /eliminate.
Native performance analysis
A working feature can still hide a scaling problem. Lamied’s proprietary C++ engine estimates time and space complexity and detects repeated database and network calls.
For every user, scan every order.
users.map(user => orders.filter(…))Initialize users, then scan orders once.
totals.set(userId, current + amount)Illustrative comparison. A Map uses extra storage; expected complexity assumes average O(1) access. Static findings are estimates—confirm them with benchmarks.
Languages parsedNative syntax-tree analysis
Time & space analysisTypeScript, JavaScript, Python, C, C++
Cross-file call tracingTypeScript, JavaScript, Python
Built for the full development cycle
Use the AI assistant to make changes across your project, run tests in the integrated terminal, and inspect the resulting diffs before you commit.
Navigate files, symbols and language-server context. Find the code that matters before making a change.
Read and edit across the project. Run your application and focused checks in the integrated terminal.
Inspect file diffs, work with Git and return to checkpoints when a task needs a different approach.
Designed for more accessible AI development
Fragmentation gives smaller models focused tasks. Dynamic Tool Generation is designed to provide task references on demand and reduce repeated context. Our goal is a lower cost per successfully completed task.
Explore plansLower AI cost per successfully completed task compared with current coding tools.
A development target. Not a measured result or a guaranteed saving.Model choice
Use NVIDIA Nemotron through NVIDIA’s hosted API, choose another provider, or connect a local model through a compatible endpoint.
Model setup and requirementsUse your provider credentials or a local model. Available features depend on your Lamied plan and configuration.
Our launch plan brings model access into one Lamied subscription, with usage allowances and local-currency payments in Ethiopia.
Individual developers and small software teams building real products. Lamied combines a desktop development environment, AI assistance and native performance analysis. It is available worldwide, with Ethiopia as our first commercial market for the planned local-currency subscription.
They describe Lamied’s execution architecture: break a complex request into precise tasks, then give small models on-demand references to each task’s input and saved output. This is the direction described in our pitch deck; the task-tool warehouse is not yet part of the current download.
No. It is our development target, not a measured saving or a guarantee. We plan to compare AI cost per successfully completed task while matching task quality, including token usage, retries and verification.
Installers are available for Linux (Debian and Ubuntu on 64-bit Intel or AMD computers) and Windows (64-bit Windows 10 and 11). There's no macOS version yet.
You can download and use the editor without an account. AI features require an eligible Lamied plan. Depending on your plan and configuration, use Lamied-hosted models, your own provider API key, or a local model. Your provider bills any usage made with your own key.
You can configure a local model through an OpenAI-compatible endpoint. Review embedding settings too: a local chat model alone does not make remote embeddings, sign-in, or other network-dependent features work offline.
Native parsing and local complexity analysis run on your machine. Cloud AI and remote embedding configurations may send code, prompts, or tool output to configured providers or services. Review those settings before using sensitive repositories.
Your next project starts here
Start with the free editor. Explore AI assistance and performance analysis on your own project.