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The Rise Of Computer-Using Agent And Sandboxes
Understanding how computer-using agents will transform our everyday lives.
Aug 22
•
Nilesh Barla
153
1
Tokenmaxxing And Return-On-Tokens
As AI becomes a crucial part of our daily workflow, Return on Tokens will be the future metric to evaluate the quality of AI-driven outcomes.
Aug 15
•
Nilesh Barla
180
1
How The Product Role Is Moving To Building And Verification
As AI speeds up execution, product leaders must focus more on product verification, evidence, judgment, and what is worth shipping.
Aug 8
•
Nilesh Barla
242
2
2
Eval-First Product Design For Frontier AI Products
The evaluation suite is the specification your frontier product actually runs on. Everything else is intent.
Jul 31
•
Nilesh Barla
241
1
What Product Leaders Should Stop Doing Now That AI Can Do It
A practical framework for removing low-leverage work without outsourcing judgment.
Jul 25
•
Nilesh Barla
175
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In the Age of Agentic Engineering, Context Is Your Real Product
Feb 28
•
Nilesh Barla
323
3
Sub-Agents For Product Managers: Stop Directing A Tool. Start Running A Team.
Mar 7
•
Nilesh Barla
262
2
Growth And Retention In An AI-first World | Takeaways For Founders And Product Leaders
Feb 4
•
Arsh Shah Dilbagi
230
1
The AI Skills No One Is Teaching Product Managers (But Should Be)
Feb 21
•
Nilesh Barla
185
1
Latest
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Discussions
What Is An Agentic Stack, And Why Does It Matter More Than the Model?
A practical guide to the routing, context, tools, verification, and control layers that turn AI models into reliable agents.
Jul 18
•
Nilesh Barla
103
2
What Is Loop Engineering, and Who Owns It?
The role that shapes how AI agents halt, hold state, and recover, plus a maturity model to score your team against.
Jul 11
•
Nilesh Barla
128
Agent Replay Is A Product Surface, Not A Debugging Feature
Agent replay for production AI agents. The trace spec, who it serves, and why to build it in from day one.
Jul 4
•
Nilesh Barla
167
1
The Long-Horizon AI Agents Ceiling Is A Product Problem
The planning ceiling is real, measurable, and not closing fast enough to be a roadmap. Five product moves that bypass it now, including the one that…
Jun 27
•
Nilesh Barla
111
1
The Self-Improving Agent Is A Production Pattern Now
Agentic harness engineering is the discipline that builds one. The five layers, what they decide, and why this is the pattern that wins production AI in…
Jun 20
•
Nilesh Barla
87
4
3
Chat Is the Wrong Default for AI Products
Chat works for exploration. For everything users do repeatedly, a button, a canvas, a delegated agent, or a background listener works better.
Jun 13
•
Nilesh Barla
109
1
Prompt Injection Is Not a Prompt Problem
The tool layer is where agents get compromised. Nobody has been looking at the right layer.
Jun 6
•
Nilesh Barla
212
1
The Operating Loop: How Production AI Agents Actually Get Better, And Where The Loop Breaks
Three orphan disciplines — observability, evaluation, improvement — make one loop. The loop breaks at the seams.
May 30
•
Nilesh Barla
66
2
What Happens When Your AI Agent Interacts With Everything
A practitioner's framework for model selection when your agent's connectivity outgrows what benchmarks measure.
May 23
•
Nilesh Barla
156
2
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Adaline Labs
The newsletter that swaps stale buzzwords for actionable insights. Our research-backed articles, expert commentary, and bold experiments with LLMs serve one purpose: to spark inventive thinking. By Adaline(.ai).
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