Try the demo

Your AI copilot for technical conversations

Ace the interview.
Explain the work.

Bring your codebase. Hear the question. Get project-grounded guidance on your phone—for interview practice and everyday project meetings.

Try the demo See how it works

Desktop app

  • Windows
  • macOS

Phone companion · in your browser

  • Android
  • iOS
Hear the answer through your earbud. No reading required. Coming next ↗

In the conversation.
With your project in context.

The question on your laptop. A clear answer on your phone. Built around the code and decisions that are yours.

Technical interviewDemo sequence
Developer
AI-generated scenes · silent demo

InterviewerQuestion captured

Spoken question

“Why did you choose streaming?”

Project context
llm.pyresponse_server.py
bTalent

Practice outline

“Start with why early output helps the user. Explain how the response streams. Then cover the trade-off: partial output and interrupted connections.”

✓ From your projectllm.py

On your paired phone

Voice replies · Coming next

Hear the answer.
Keep your eyes on
the conversation.

Short, quiet guidance through your earbud. For interview practice and online meetings, without reading from your screen.

Watch the full journey: a question, your project context, the bTalent app and our planned voice replies.

  1. 01Question
  2. 02Project context
  3. 03In your ear
bTalent in action
Read the walkthrough
  1. The question. A meeting participant asks, “Why did you choose streaming?”
  2. Your project. An approved, read-only snapshot gives bTalent the context in llm.py and response_server.py.
  3. The bTalent app. The response appears in the feed with source references. The example explains early output, streamed updates and interrupted connections.
  4. Voice replies · Coming next. The earbud concept says, “Start with the trade-off. Streaming shows the answer as it arrives.”

Real app interface with illustrative example data. AI-generated people. Edited pacing, not measured response time.

FAANG & AI interview preparation

Prepare to ace your next technical interview.

Turn your own projects into clear answers. Practise explaining your architecture, trade-offs and implementation decisions before a high-stakes interview.

Prepare for “Why this architecture?” and “What would you change?” with answers that refer to the code and decisions in your own project.

Try the interview demo

For practice and interviews where AI assistance is allowed and disclosed.

Interview · demo sequence
The interviewer asks

“Why did you
choose streaming?”

llm.pyresponse_server.py
Your answer outlinePractise in your own words

“Start with why early output helps the user. Explain how the response streams. Then cover the trade-off: partial output and interrupted connections.”

Where you’re aiming

Independent interview preparation. No affiliation or endorsement.

  • Google
  • Meta
  • Apple
  • Netflix
  • OpenAI
  • xAI

Project meetings

Understand the code you shipped.

Get a map of the code you shipped. Explain the architecture and answer specific questions in demos, reviews and client handoffs.

Project briefingDemo sequence
capture.pyPython · Image preparation
if img.width > max_width:
    ratio = max_width / img.width
    img = img.resize(
        (max_width,
         int(img.height * ratio)),
        Image.LANCZOS)
img.save(buf, format="JPEG",
         quality=quality)

Source: bTalent demo project

A teammate asks

“Why does image quality affect response time?”

Your explanation

“Screenshots are resized and JPEG-compressed before they reach the model. More detail increases the payload, so we balance readable input with transfer time.”

Try the meeting demo

Know the project.
Before you explain it.

A briefing before the meeting. A review before the demo. A precise answer when someone asks how it works.

Project workspaceDemo project
capture.pyllm.pyresponse_server.py
Meeting briefing

“Give me the project walkthrough.”

A compact map of the capture-to-answer flow, ready to turn into your own explanation.

  1. The inputA screenshot is resized and JPEG-compressed before it reaches the model.
  2. The responseThe model streams its answer, so the phone can show text as it arrives.
  3. The trade-offBalance readable image detail with the size of the request.
capture.py:18capture.py:23llm.py:594
Illustrative output · read-onlyExplore the project demo

The details that
make it yours.

From the first practice question to the next project handoff. Bring the context you need, in the way that suits the conversation.

01 / Spoken questions

Speak in English. Or Greek.

Use microphone or system audio to bring spoken questions into the conversation. Prefer typing? Ask your project directly.

Why this architecture?Γιατί αυτή η αρχιτεκτονική;

02 / Screen context

Ask about what’s on screen.

Capture a code snippet, an error or a diagram. Keep selected screenshots as temporary context when the question spans more than one view.

CaptureAdd contextAsk

03 / Interview practice

Prepare for your next role.

Add a role or job description to focus your preparation. Get an answer outline, what the question tests and pitfalls to practise in your own words.

What they’re testingAnswer outlinePitfalls

04 / Editor & project history

Your code. And what changed.

Approve a VS Code snapshot with recent Git changes and optional Claude Code or Codex session context. Explain the decisions behind the latest work.

VS Code snapshotGit changesAI session context

05 / Phone companion

Keep the answer within reach.

Open the companion in your Android or iOS browser. Read streaming responses, copy formatted code and revisit earlier answers for the current app session.

Streaming textCode blocksSession history

06 / Context controls

You choose what to share.

Review the upload summary before sharing it. Secret files are excluded and reviews leave your code untouched. Approved context goes to your configured AI provider.

Approve snapshotRead-only reviewClear context

See a project question answered.

Choose an interview or meeting. Explore prewritten examples from bTalent’s own codebase.

bTalentInteractive demo · prewritten replies
Project walkthrough Local demo
The question

“Why does image quality affect response time?”

bTalent suggests

Screenshots are resized and JPEG-compressed before they reach the model. More detail increases the payload, so resolution and quality are a trade-off between readable input and transfer time.

Check the image settings in config.py and the capture pipeline in capture.py.

capture.pyconfig.py
Try another question
  1. 01
    Share your project

    Approve a VS Code snapshot.

  2. 02
    Bring the question

    Speak, type or capture your screen.

  3. 03
    Read your answer

    Follow along on your paired phone.

Product details.

Read-only access.
Temporary snapshots.

What can I use today?

Project briefings, codebase Q&A, read-only reviews, screen questions and spoken input. Responses stream to your phone.

How does interview practice work?

Add your role or job description, then rehearse a question. Interview mode explains what it tests, gives you an outline and flags pitfalls. In live interviews, follow the interviewer’s rules and disclose permitted AI assistance.

Which devices can I use?

Run the desktop app on Windows or macOS. Open the paired phone companion in your Android or iOS browser.

Does it change my code?

No. Reviews are read-only. Approved snapshots stay in app memory, with secret files excluded.

What does spoken input need?

English and Greek transcription require the local audio setup. The current packaged Windows build focuses on screen capture.

Can I hear answers in my ear?

Voice replies are coming next: short, quiet guidance through your earbud, so you can follow the conversation without reading. The video shows the intended experience. Today, spoken questions produce text answers on your phone.

Is the demo connected to an AI?

No. It uses prewritten replies and never accesses your microphone, screen or files.