01 / REPRESENT
Code, in a new format.
Render readable source-code images with adjustable density. Keep the original text whenever exact symbols matter.
VISUAL CONTEXTTHE VISION-NATIVE CODING WORKSPACE// 001
See more
than text.
Code smarter.
Turn source code into visual context. Connect a model on your machine or use leading AI APIs to inspect code images and reason over a project—with full control of what gets sent.
01 Browser-local tools02 Local or cloud vision03 Measured, not guessed
lkxgo / visual context LIVE STUDIO
01 SOURCE / typescript •••
01export async function
02inspectRepository(repo) {
03 const files = await
04 repo.findRelevant();
05 return files.map(
06 file => render(file)
07 );
08}
VISUAL CONTEXT↓
code_context.png
02 VISION MODEL
LOCAL↔CLOUD
✳
Understand the code visuallyChoose the model. Verify the answer.
BROWSER-LOCAL RENDERINGPNG → VLM
◎
LOCAL + APIYour model, your choice.
LKXGO / BUILT FOR A DIFFERENT PERSPECTIVESCROLL TO EXPLORE ↓
✳ CODE → IMAGE✳ LOCAL VISION✳ CLOUD APIS✳ CONTEXT CONTROL✳ TASK-AWARE SEARCH
[ 01 / THE DIFFERENCE ]
LKXGO / IDEA 001
Code is more than
a wall of tokens.
Sending every file as plain text isn't always the best way to work. LKXGO lets you inspect relevant files, turn code into readable images, and test how vision-capable models interpret them.
02 / ROUTE
Freedom to choose a model.
Use a compatible Ollama / vLLM model locally, or bring your own OpenAI / Claude API key for a one-off vision query.
MODEL FLEXIBILITY03 / MEASURE
Efficiency you can verify.
Inspect reported usage where available. Real savings depend on the model, image detail, the task, and possible retries.
REAL-WORLD COST[ 02 / THE MODEL LAYER ]
ANY VISION-CAPABLE MODEL
Your code.
Your intelligence.
Keep image rendering local. Send a code image only when you explicitly run a model request. Use the provider that fits your privacy, hardware and budget.
01 / LOCAL
On your machine.
Connect to Ollama or an OpenAI-compatible vLLM server with a vision-capable model. Your model does the reasoning on your hardware.
OllamavLLMPrivate setup
02 / CLOUD
APIs on demand.
Use an OpenAI or Anthropic vision-capable model with your own temporary API key. You control every outgoing request.
OpenAIClaudeBYOK
03 / WORKFLOW
Keep the best of both.
Use local tools to inspect and render. Compare local and cloud responses when needed; keep precise source text for edits.
VISUAL + TEXTHuman-controlled
Explore vision lab ↗[ 03 / INTERACTIVE STUDIO ]
AVAILABLE NOWFrom source code
to visual understanding.
The code image generator works entirely in your browser. Optionally run a vision request using a configured model. Real results, no simulated AI response.
01 / SOURCE CODE
IMAGE DENSITYBalanced readability
CTRL / ⌘ + ENTER
02 / IMAGE PREVIEW—
Paste code to render an image.
Ready
03 / ASK A VISION MODEL
RESULT / MODEL RESPONSEIDLE
Choose a model, enter your question and run an analysis. Responses appear here; nothing is fabricated.
Usage will be shown when returned by the provider.
✳Images are generated locally. Model queries send the current code image and question to the selected endpoint only after you click Analyze. API keys remain in page memory and are not saved.
[ 04 / LOCAL REPOSITORY ]
NO UPLOAD · NO ACCOUNT
Find the right files.
Skip the noise.
Choose a folder from your computer, describe a task, and explore the files most likely to matter. Search is local keyword matching—not an AI model—and never modifies your source.
⌁REPOSITORY EXPLORER
YOUR FILES STAY LOCALRELEVANT FILES0
Select a project folder to begin.
SOURCE PREVIEWWAITING
// LKXGO / local repository explorer
Local search · file previews are read-only
[ 05 / A BETTER PRINCIPLE ]
A smaller prompt
isn't the whole story.
Code images may reduce some text context, but visual processing also consumes tokens or GPU time. Evaluate the entire task: model cost, answer quality, follow-up calls and failures.
Run your own comparison ↗OPTIMIZATION ≠ COMPRESSION ALONE
01 Relevant contextFILTER
02 Readable representationCHECK
03 Observed usageMEASURE
Illustrative bars—not performance measurements.
[ 06 / FAQ ]
NO BLACK BOX
A few important
details.
Useful AI tooling should explain what actually happens to your code, your keys and your compute.
Can I use my own local vision model?+
Yes. Set up a vision-capable Ollama model or an OpenAI-compatible vLLM server. In-browser connections can require explicit CORS and browser network permissions; the local server is separate from this website.
Can I call OpenAI or Claude directly?+
The studio can send a request with a manually entered API key directly from your browser. Keys are never stored, but browser-direct keys have security risks and may be restricted by provider or browser policies. For production and multi-user deployments, use a secure server-side proxy.
Does LKXGO upload my entire repository?+
No. Folder selection, keyword ranking and code-image rendering happen in your browser. Only the currently rendered image and your question are sent to a model endpoint when you explicitly click Analyze.
Is this a fully autonomous agent that edits code?+
This static website provides a working vision and repository workspace with optional model inference. Autonomous file edits, tests and pull requests require a separately deployed, permissioned agent runtime and execution sandbox.
Will vision always save tokens?+
No. Vision pricing, image size, model accuracy and retry costs differ. Compare real provider usage and task results before deciding whether visual context is more efficient.
LKXGO / MAKE CONTEXT WORK SMARTER
Build with
a new perspective.
Open the working tools, bring a vision-capable model, and see what a different representation of code can unlock.
Launch vision studio ↗CODE · CONTEXT · INTELLIGENCE EST. 2026 / LKXGO