Latest / AI Fire Daily / #646 Neil: Claude Prompt Tips For More Reliable Opus 5.5 Results

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#646 Neil: Claude Prompt Tips For More Reliable Opus 5.5 Results · AI Fire Daily

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  If you are still typing think carefully or think
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  step by step into your AI prompts, you can stop.
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  Opus 5 .5 has already evolved past that. It really
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  has. I mean, the whole era of micromanaging the
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  machine is officially dead. Honestly, holding
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  on to those old habits is just slowing you down
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  now. Welcome to this deep dive. I am really glad
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  you are here with us. We are taking things at
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  a measured, thoughtful pace today. Yeah, it is
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  a big topic. Our mission today is to explore
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  a definitive guide we found. It fundamentally
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  changes how we prompt Cloud Opus 5 .5. We are
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  going to cover why micro -managing is out, and
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  how to guide long -running autonomous tasks.
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  Right. And we will get into making the results
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  bulletproof, plus some real -world workflows.
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  Because the AI is so smart now, you know, the
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  bottleneck isn't its reasoning. The bottleneck
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  is actually our ability to define the finish
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  line. Exactly. So, let's unpack this new prompt
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  structure. We have to shift our mindset from
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  step -by -step guide to visionary delegator.
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  Which is a huge mental leap. It is? Let's look
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  at the guide's prime example. Say you need to
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  migrate payment endpoints across a massive code
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  base. Before, you would feed it one file at a
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  time. Yeah, you would say, fix this file, now
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  wait for my next command. It was tedious. But
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  with Opus 5 .5, you start with the full task.
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  You give it the whole job up front. You just
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  hand it over. Exactly. You tell it, migrate all
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  payment and points to the new client, remove
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  the old one, and leave unrelated parts alone.
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  It's like handing a master chef a menu instead
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  of teaching them how to chop onions. That is
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  a brilliant analogy. Yes. You are giving it the
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  context and letting it run. But I have to admit,
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  I still wrestle with prompt drift myself. It's
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  hard to let go of the steering wheel. Oh, totally.
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  It feels risky, which is why you have to clearly
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  define what done actually means. Right. You set
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  a strict checklist. All tests pass, the old client
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  is fully removed, and the final diff is checked.
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  Yes. And you have to set clear constraints. You
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  keep it from helping too much. Like, you tell
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  it to keep the info architecture and avoid pill
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  -shaped buttons or monospace labels. Let me ask
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  you this. If we don't set those visual constraints,
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  like no pill -shaped buttons, does it just hallucinate
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  a redesign? Yes. It assumes broad helpfulness,
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  so boundaries keep it strictly on task. So strict
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  boundaries prevent the AI from quietly redesigning
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  the whole app? Precisely. And one more crucial
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  thing from the guide. You have to remove those
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  think hard instructions. Anthropic actually found
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  that removing them makes responses start faster
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  without dropping quality. That makes a lot of
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  sense. Save the tokens for your actual constraints.
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  Exactly. OK, so that covers how to ask. But what
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  happens when a task takes hours and spans a whole
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  code base? That is where it gets really interesting.
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  you have to establish stop rules. The guide recommends
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  putting these in a Claude .md file, right? Yeah.
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  You use the Claude .md file to stop it from pausing
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  just to report progress. You tell it to keep
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  going unless it hits a crucial decision. Or a
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  dangerous one. Right. It should only stop if
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  an action could delete data, overwrite human
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  work, force push a repository, or change things
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  outside the repo. That feels like a solid safety
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  net. But what if you realize halfway through
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  that you forgot a requirement? You use in -flight
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  instructions. You just add the new instructions
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  into the chat without restarting. Because restarting
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  a massive run is incredibly expensive. Right.
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  Restarts destroy your context window. So you
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  just drop a message saying, keep existing endpoint
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  names as aliases so older integrations work.
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  It adapts dynamically. It adapts dynamically
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  and it's so fast. That is amazing. But over a
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  long run, how does it remember everything? The
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  guide says to keep tasks in a tasks .md file.
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  Yeah, long sessions lose memory context. A plain
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  text file stays reliable. Cloud must check items
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  off and append new discovered work there. And
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  if the task is too big, even for that? Then you
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  split the work using subagents. Let's define
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  that quickly. Smaller AI helpers working on different
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  parts of a problem simultaneously. Exactly. Like
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  auditing a retry bug. The main agent splits the
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  work across a dozen subagents in parallel. Wow.
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  Imagine scaling parallel subagents to audit an
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  entire codebase simultaneously. That's profound.
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  It really is a whole new way of working. Totally.
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  Why use a separate .tasks .md file instead of
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  just letting the AI remember its progress? Because
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  in very long sessions, the underlying memory
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  context gets compressed and summarized, risking
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  dropped tasks. Right. Memory fades, but a dedicated
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  text file stays perfectly reliable. Exactly.
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  We are going to take a brief pause here. When
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  we come back, we will talk about how to actually
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  trust the results of all this autonomous work.
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  Sounds good. If you spend hours deep in source
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  material, you know the physical toll it takes.
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• 5:05
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• 5:08
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• 5:11
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  the foundation for deep work. Okay, we are back.
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  The autonomous run is finished, but how do we
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  actually trust what it hands back? You have to
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  force a final review step. You literally have
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  Opus 5 .5 review its own final diff or PR for
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  incorrect or out of scope issues before saying
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  done. That is a great trick. What about for research
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  tasks? For research, you demand it marks unconfirmed
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  info. It has to state what it could not verify,
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  where it checked, and what evidence is missing.
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  It forces it to admit its blind spots. Exactly.
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  And you require hard evidence. Say you are auditing
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  a notification delivery flow, checking retries,
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  timeouts, and fallbacks. If it finds a risk,
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  it must provide the exact code evidence. If the
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  evidence is weak, it has to mark the finding
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  as uncertain. By forcing it to provide evidence
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  for a timeout risk, are we essentially making
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  it show its math? Exactly. You prevent it from
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  just claiming an issue exists without pointing
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  to the exact line of code. Yeah. We are demanding
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  proof so we never have to trust it blindly. Right.
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  And to digest all this, you demand a clear status
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  summary at the end. You ask for exactly three
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  sections. Blocked on me, changed, and found.
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  Yes. It makes the final output incredibly clean
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  and actionable. So let's bring this all together.
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  How do we apply these patterns to real daily
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  office work? Well, say you have a messy service
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  diagram screenshot. You can share visuals directly.
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  You just upload the image? Yeah. You ask it to
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  trace which services call the billing API directly
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  and literally point to the exact arrows. That
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  saves so much time. The guide also mentions auditing
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  long documents. Oh, this is a lifesaver. You
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  give it a massive document and ask it to find
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  internal contradictions, like dates, numbers,
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  or names that conflict across distant sections.
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  When checking distant sections of a document
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  for contradictory numbers, does it ever get confused
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  by context? It can, which is why instructing
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  it to quote the conflicting parts and explain
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  why they don't match is crucial. Got it. Asking
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  for direct quotes forces it to anchor the contradiction
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  in reality. Exactly. And when you want the data
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  extracted, you ask for the finished file. Right.
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  You don't just accept a markdown table. No. You
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  tell it exactly what you want. A spreadsheet
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  with columns for vendor, annual cost, owner,
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  and renewal status. Right. And the spreadsheet
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  is actually ready to share. Yes. It is fully
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  formatted and ready to go. One last pattern from
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  the guide. Keeping projects focused. Yeah, you
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  tell it to treat earlier answers as settled unless
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  new evidence appears. It avoids endless second
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  guessing. That keeps the momentum going perfectly.
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  It really does. So synthesizing everything we
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  have discussed today, what is the core philosophy
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  here? The big idea is that OPUS 5 .5 requires
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  a shift from treating prompting like a bag of
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  tricks to giving it a clear job. You define the
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  result, set boundaries, let it move, and force
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  it to verify its own work. It is a complete paradigm
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  shift. Thank you for joining us on this deep
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  dive. It was a fun one. We have covered a lot
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  of ground today. But I want to leave you with
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  one final lingering thought to mull over. OK.
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  If we are now teaching AI to meticulously document
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  its own limits, ask for permission before deleting
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  data, and manage its own subagents, how long
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  before the AI simply starts writing its own stop
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  rules? That is a wild thought. Keep exploring