Talqo - AI Interview Copilot for macOS
Talqo is a native macOS interview copilot that runs entirely on your machine. Choose local or cloud speech-to-text. Full audit trail on disk. No servers. No accounts. Complete privacy.
Every interview copilot on the market routes your audio, your transcripts, and your screen captures through their servers. You don't choose the STT provider. You don't see what they send to their AI. You don't know what they store.
Cloud-based copilots store your transcripts on their servers. If those servers are breached, your interview history is exposed. Talqo stores everything locally on your machine. No servers, no breach surface.
Talqo takes a fundamentally different position. You choose your STT provider - local (nothing leaves your Mac) or AssemblyAI (SOC 2 certified, audio deleted after processing). The only other outbound call is to Anthropic's API, using your own key. Every call is logged to local files you can inspect any time. No Talqo servers exist. There is nothing to breach.
System architecture showing Your Mac, Anthropic API, Their Servers, Their DB, Their LLM
AI Interview Modes
Screenshot. Analyze. Solve.
Press Cmd+1 to capture your screen. Talqo analyzes the coding problem, whiteboard diagram, or system design prompt with vision - and delivers a structured solution in seconds.
> Analyzing screenshot...
The problem asks for the maximum subarray sum.
Use Kadane's algorithm - O(n) time, O(1) space:
def max_subarray(nums):
current = best = nums[0]
for n in nums[1:]:
current = max(n, current + n)
best = max(best, current)
return best
Full conversation context.
The entire transcript feeds into the AI. Talqo knows what the interviewer asked, what you said, and what was discussed 20 minutes ago. Responses reference the actual conversation, not generic templates.
> Based on your transcript + memory files
Situation: At my previous company, our payment
processing service was hitting 3s p99 latency during
peak hours - directly impacting checkout conversion.
Task: I was responsible for redesigning the service
to handle 10x traffic within the existing infra budget.
Action: I introduced a write-behind cache with Redis,
batched database writes, and moved to async processing.
Result: p99 dropped to 180ms. Checkout conversion
increased 12% in the following quarter.
The questions they don't expect.
Generate insightful follow-up questions based on the full conversation context. Show the interviewer you were listening - with questions that prove deep understanding of their architecture, constraints, and trade-offs.
> 3 follow-up questions based on transcript
1. You mentioned the migration from monolith to
microservices took 18 months - what was the
strategy for handling shared state during the
transition period?
2. Given the team uses event sourcing for the order
pipeline, how do you handle schema evolution
without breaking downstream consumers?
3. You said the on-call rotation covers 40 services -
what does the runbook structure look like, and how
do you keep them current as services evolve?
Ask anything. Full context included.
Type any question or instruction. Talqo sends it with the full transcript, memory files, and session context. Use it to explore tangents, get explanations, or prepare for curveball questions on the fly.
> You: "Explain the CAP theorem trade-offs for their
architecture - they use Cassandra + Redis"
Their stack suggests AP over CP:
Cassandra: partition-tolerant, eventually consistent.
Redis: in-memory, single-leader replication.
Key trade-off to mention:
They likely accept stale reads during partitions
(AP) but use Redis for hot-path consistency where
strong reads matter (e.g., session state).
Good follow-up: "How do you handle the consistency
gap between Cassandra and Redis during failover?"