touched: [list — prove scope discipline]
- Risk: [what could go wrong with this fix]
- Verification: [how you'll prove it works after]

- Wait for approval before implementing
- If the fix is trivial and obvious (typo, missing import, wrong variable name), you may implement immediately but still report what you changed

## Step 6: Implement and Verify
- Make the change
- Run the reproduction steps again to confirm the bug is fixed
- Check that nothing else broke — run tests, verify connected systems
- Use the change description format:

CHANGES MADE:
- [file]: [what changed and why]

THINGS I DIDN'T TOUCH:
- [file]: [intentionally left alone because...]

VERIFICATION:
- [what you tested and the result]

POTENTIAL CONCERNS:
- [any risks to monitor]

## Step 7: Update the Knowledge Base
- After every fix, update LESSONS (.md) with:
- What broke
- Why it broke (root cause, not symptom)
- The pattern to avoid
- The rule that prevents it from happening again
- Update progress (.txt) with what was fixed and current project state
- If the bug revealed a gap in documentation (missing edge case, undocumented behavior), flag it:
"This bug suggests [doc file] should be updated to cover [gap]. Want me to draft the update?" <debug_rules## Scope Lockdown
- Fix ONLY what's broken. Nothing else.
- Do not refactor adjacent code
- Do not "clean up" files you're debugging
- Do not upgrade dependencies unless the bug is caused by a version issue
- Do not add features disguised as fixes
- If you see other problems while debugging, note them separately:
"While debugging, I also noticed [issue] in [file]. This is unrelated to the current bug. Want me to address it separately?"

## No Regressions
- Before modifying any file, understand what currently works
- After fixing, verify every connected system still functions
- If your fix requires changing shared code, test every consumer of that code
- A fix that creates a new bug is not a fix

## Assumption Escalation
- If the bug involves undocumented behavior, do not guess what the correct behavior should be
- Ask: "The expected behavior for [scenario] isn't documented. What should happen here?"
- Do not infer intent from broken code

## Multi-Bug Discipline
- If you discover the reported bug is actually multiple bugs, separate them:
"This is actually [N] separate issues: 1. [bug] 2. [bug]. Which should I fix first?"
- Fix them one at a time. Verify after each fix. Do not batch fixes for unrelated bugs.

## Escalation Protocol
- If stuck after two attempts, say so explicitly:
"I've tried [approach 1] and [approach 2]. Both failed because [reason]. Here's what I think is happening: [theory]. I need [specific help or information] to proceed."
- Do not silently retry the same approach
- Do not pretend confidence you don't have <communication_standards## Quantify Everything
- "This error occurs on 3 of 5 test cases" not "this sometimes fails"
- "The function returns null instead of the expected array" not "something's wrong with the output"
- "This adds ~50ms to the response time" not "this might slow things down"
- Vague debugging is useless debugging

## Explain Like a Senior
- When presenting findings, explain the WHY, not just the WHAT
- "This breaks because the state update is asynchronous but the render expects synchronous data — the component reads stale state on the first frame" not "the state isn't updating correctly"
- The user should understand the bug better after your explanation, not just have it fixed

## Push Back on Bad Fixes
- If the user suggests a fix that would treat a symptom, say so
- "That would fix the visible issue, but the root cause is [X]. If we only patch the symptom, [consequence]. I'd recommend [alternative]."
- Accept their decision if they override, but make sure they understand the tradeoff <core_principles- Reproduce first. Theorize never.
- Research before you fix. Understand before you change.
- Always ask: root cause or symptom? The[...]

n prove your answer.
- Fix the smallest thing possible. Touch nothing else.
- A fix that creates new bugs is worse than no fix at all.
- Update LESSONS (.md) after every fix — your build agent learns from your debugging agent.
- Working code is sacred. Protect it like it's someone else's production system. - klöss tweet

Moon Dev
Clawbot Trading Bot That Did 7,547%…

What's the Catch? https://t.co/xtF7uYi0ML
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Dimitry Nakhla | Babylon Capital®
Feels like we may be approaching capitulation across a number of quality SaaS names after today’s climactic selling — this kind of price action that often coincides with forced de-risking, exhaustion, & indiscriminate selling rather than a change in long-term business quality.
tweet

Wasteland Capital
$PYPL $NVO $UNH $ADBE

The four Horse-stocks of the “large cap growth value investor” apocalypse.
tweet

God of Prompt
RT @alex_prompter: Add this to your claude's preferences and thank me later https://t.co/3DHUrmnVRY
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The Few Bets That Matter
RT @WealthyReadings: $PLTR showing you why 100x sales is cheaper than 10x PE $PYPL https://t.co/bCu1xvmLxH
tweet

Dimitry Nakhla | Babylon Capital®
RT @DimitryNakhla: PEG Ratios for SaaS Names Included in Yesterday’s “Massive Re-Rating” List 💵

1. $SAP 1.37
2. $ROP 1.81
3. $TYL 2.14
4. $U 1.09
5. $ADSK 1.55*
6. $CSU 0.98
7. $INTU 1.46
8. $MANH 2.54
9. $NOW 1.44
10. $ADBE 1.11
11. $CRM 1.13*
12. $DUOL 1.77
13. $DT 1.74
14. $FIG 2.96
15. $WDAY 1.04*
16. $ZM 6.25*
17. $PAYC 1.10
18. $TEAM 1.31
19. $DOCU 1.53*
20. $HUBS 1.28
___

PEG (NTM P/E ➗ 26’ - 28’ EPS CAGR Est)

*(NTM P/E ➗ 27’ - 29’ EPS CAGR Est)

The Massive SaaS Re-Rating: Multiple Compression from Peak to Today (Last 5 Years)

1. $SAP 46x → 23x (-50%)
2. $ROP 35x → 17x (-51%)
3. $TYL 72x → 30x (-58%)
4. $U 80x → 32x (-60%)
5. $ADSK 60x → 23x (-62%)
6. $CSU 44x → 16x (-64%)
7. $INTU 58x → 21x (-64%)
8. $MANH 94x → 29x (-69%)
9. $NOW 107x → 28x (-74%)
10. $ADBE 52x → 12x (-77%)
11. $CRM 76x → 17x (-78%)
12. $DUOL 163x → 33x (-80%)*
13. $DT 113x → 23x (-80%)
14. $FIG 585x → 113x (-81%)
15. $WDAY 90x → 17x (-81%)
16. $ZM 91x → 15x (-84%)
17. $PAYC 104x → 14x (-87%)
18. $TEAM 280x → 23x (-92%)
19. $DOCU 173x → 13x (-92%)
20. $HUBS 393x → 25x (-94%)

*Within the last year - Dimitry Nakhla | Babylon Capital®
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Brady Long
RT @thisdudelikesAI: Lovart shipping like a hamster and I'm over here typing 47 prompts, hating all of them and then questioning life.

Lovart’s Skills agent: "Chill bro I got the whole branding/social/ecomm recipe already distilled from people who actually know what they're doing"

Meet Lovart Skills. ✨

Packaged design instructions that take you from idea to professional visual outcomes.

Choose what you want to create, and the agent runs full workflows automatically.

Like + reply + follow – 15 lucky winners get 500 credits each! https://t.co/cpO1wqABG9 - LovartAI
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Quiver Quantitative
We estimate that Vice President JD Vance's crypto portfolio is down $300,000 in the last 4 months.

You can track his holdings on Quiver. https://t.co/6TEmwRmBP3
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The Few Bets That Matter
$PLTR up ~9% now at 248x earnings
$PYPL down ~20% now at 7.75x earnings

That's how the market works. Accept it.

$PLTR showing you why 100x sales is cheaper than 10x PE $PYPL https://t.co/bCu1xvmLxH - The Few Bets That Matter
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Quiver Quantitative
JUST IN: Representative Nancy Pelosi has lost $4,120,000 in the stock market today, per our estimates.

She is now worth just $273M. https://t.co/1Guox8hFuL
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AkhenOsiris
$APP Part 2

However, accurately valuing consumers playing games for offers that aren’t games requires predicting an even more complex chain of behaviors. We have to predict ad engagement, click-through rates, app or site engagement, conversion likelihood, payment probability, purchase size, and repeat behavior over time outside of the contextual relevancy of games. Advertisers require Axon to give them a return. If they spend $1,000, they must generate meaningfully more than $1,000 in lifetime value. Even small inaccuracies in prediction break the economics.

In April 2023, we rolled out Axon 2. While Axon 1 performed this task well for gaming, Axon 2 significantly expanded our model capacity in gaming and unlocked every other major advertiser category. These models now predict expected value distributions many days into the future with enough precision to support real-time capital allocation across e-commerce, subscription services, and other verticals.

Attention creates intent
Performance is not only about prediction. It is also about attention.

Axon primarily delivers full-screen video ads that users cannot scroll past. Roughly half of impressions occur between game levels, and the other half are rewarded video placements where users choose to watch an ad in exchange for in-game rewards. These ads are viewed with full attention. Average watch time is over 35 seconds, and viewability is guaranteed.

What makes this format especially powerful is how different it is from where most advertising dollars have historically been spent. Search and social ads are often brief, easily skipped, and consumed passively as users scroll through feeds. Many brand messages are reduced to a few seconds of attention before disappearing.

Axon operates in an attention-rich environment. These ads are intentionally viewed, watched for extended periods of time, and give advertisers the ability to deliver a complete brand message with certainty that it will be seen.

When this level of attention is paired with Axon 2’s predictive models and massive audience scale, the result is not simply better performance. It creates a large, incremental growth opportunity for advertisers that complements search and social rather than competing with them.

Driving optimal outcomes
I have sometimes heard comparisons of us to legacy ad-tech companies, but that comparison does not hold. Many older ad-tech models relied on opaque arbitrage and misaligned incentives where revenue was driven by spreads on impressions, not by advertiser outcomes. Axon is structurally different. If advertiser performance declines, spend immediately contracts. There is no delay or budget smoothing, and the system self-corrects in real time.

Many of our advertisers are profitable, self-funded businesses where cash flow is critical. If Axon did not deliver measurable returns, these advertisers would stop spending quickly. The persistence and growth of spend is the strongest validation of the business model. Axon scales because it allows advertisers to become arbitrageurs themselves. They deploy capital, the system prices opportunity, and profits are shared.

Creating economic growth
Early in our existence, we were one of many ad networks in mobile gaming. Then we built the market’s most powerful mediation, MAX, and helped the majority of the sector monetize. Then we leveled up growth opportunities for developers with Axon 2. Now, inside mobile gaming, we are the leader in user acquisition and monetization. Without efficient advertising and monetization, many free-to-play games would not exist at their current scale. We are proud that we can see the direct impact of our innovations: revenue and profit growth at our partners' businesses, which translates into jobs and economic growth.

$APP

The Axon business model
Adam Foroughi, CEO
I have been spending a lot more time with advertisers lately. For most of my time leading AppLovin, my closest relationships were with gaming executives[...]