I built an autonomous AI operating system that turns goals into completed work
I spent years watching talented people waste hours moving information between
emails, spreadsheets, CRMs, documents, calendars, and internal systems.
That’s when I started developing Lynx OS.
Lynx isn’t a chatbot; it’s an autonomous agent.
You define the desired outcome. It creates the plan, organizes tasks, selects the
necessary tools, and executes the work by integrating apps, files,
the browser, and the terminal.
For example:
“Process the email with the latest proposal.”
Lynx can:
• Locate and read the email
• Extract data such as contact details, company, budget, and requirements
• Query or update HubSpot
• Save attachments to Google Drive
• Draft a response
• Create a follow-up task
• Wait for approval before sending
Once created, the workflow can be reused via a simple
message or run on a schedule.
I developed the platform as a solo founder, and 58 users from the initial beta phase
have already joined through organic channels.
Lynx OS is already live, and I’m looking for founders and operators willing to
test a real-world workflow and tell me where it works well—or where it fails.
Which repetitive process would you trust an autonomous AI with first?
Try Lynx OS: [https://lynx.apex7ai.com/] Product Hunt: [https://www.producthunt.com/products/lynx-6?launch=lynx-7]
Replies
The moving between emails, spreadsheets, CRMs, and calendars part is the exact tax I used to pay every morning across a software company, a care home, and a cafe, not because any single tool was bad, just because nothing connected them. The process I would trust an autonomous agent with first is not the flashy one, it is the boring follow up, checking whether a lead or client actually got a reply. The failure mode that would worry me most is not that it misses something, it is that it acts confidently on something it should have flagged as unsure. I built FounderFlow around grading its own confidence for exactly that reason, a wrong answer stated plainly is far less dangerous than a wrong answer said with full certainty. Does Lynx have a way to say it is not sure, or does it always act.
@stacywycof83995
That is a very important distinction.
I don’t think Lynx should take action simply because the model appears confident. Models
can display a great deal of certainty and still be wrong.
We handle this in layers, rather than relying on a single confidence score.
At the platform level, Lynx follows conservative internal guidelines for risky
actions. It must pause before sending external communications, publishing
content, altering sensitive records, or performing any destructive action.
At the agent and workflow levels, users can make these rules even stricter. For
example:
“Never send an email without my review.”
“Do not delete anything.”
“Flag the task if you cannot confirm whether the client has already received a
response.”
The user can also review the execution plan and instruct Lynx to ask for confirmation before specific steps.
Memory is another important aspect. Over time, Lynx builds persistent context regarding user preferences, previous instructions, and the way the user typically conducts a workflow.
However, more context should make the result more relevant—it shouldn't silently grant the agent more permission.
I am also aware that different models exhibit varying levels of reliability. For high-impact tasks, I still believe human review is necessary, especially before something is sent, published, altered, or executed externally.
Therefore, for us, uncertainty must alter behavior:
Pause.
Explain what is missing.
Consult the user.
Your example illustrates this perfectly:
“No response found” is an evidence-based result.
“I cannot verify if a response was sent” indicates uncertainty and should be
reviewed.
That second situation should never automatically turn into permission to contact the person again.
I’m curious to know how FounderFlow handles this in practice. Is confidence based on the model's own assessment, available sources, specific workflow rules, or a combination of these factors? And what causes the system to pause and consult the user?
@apex7ai Levy, combination, and closer to your layered approach than a single score. Confidence is not the model grading its own certainty in isolation. It depends on whether what it is looking at can be checked against something firmer, a number that reconciles, a source that agrees with itself, a pattern that has held before. No firmer check, no high confidence, no matter how sure the model sounds on its own. That is what pushes something into Needs Review or Monitor Only instead of Verified. On the pause question, anything that would act outward, contacting someone, changing a record, sits behind a stricter bar than something that only surfaces information for me to read myself. Surfacing a flag can happen at lower confidence because the cost of being wrong is my attention. Acting on someone else's behalf cannot carry that same margin.
@stacywycof83995 @stacywycof83995 That makes a lot of sense.
I was thinking mainly about permissions and human approval, but your point adds
another important layer: the agent should show what evidence supports a
conclusion and reduce its autonomy when that evidence is incomplete.
Today, Lynx can already use playbooks, knowledge bases, cross-check information
between tools, and pause before sensitive actions. I can also see value in
making states like Verified, Needs Review, and Monitor Only more explicit in
the product, instead of leaving that judgment only to the model.
That is a very useful direction for us. Does FounderFlow show the evidence
behind each status, or mainly the final classification?
@apex7ai it shows the evidence, not only the final label. When something lands on Verified, you can see what it checked against, a number that reconciled or a source that agreed with itself. When something sits at Needs Review, you can see what was missing or conflicting that kept it from clearing that bar. A label with no reasoning behind it just moves the trust problem somewhere else instead of solving it. I built it that way because I did not trust my own gut checks across three businesses either, I needed to see the why, not only the verdict.