Offshore
God of Prompt RT @alex_prompter: Steal this mega prompt before I delete it 👇 The top 1% are turning Claude into Alex Hormozi, Gary Vee, and other viral marketing legends. I reverse-engineered their exact frameworks into one mega prompt. This mega prompt…
Build tension with the obstacle
- Share the breakthrough moment
- Present actionable framework
- Close with vulnerable insight + soft CTA
- Use short paragraphs, lots of white space
INSTAGRAM (GARY VEE VOLUME):
- Carousels: One idea, 10 slides
- Reels: Hook in 1 second, value in 15-30 seconds
- Stories: Behind-the-scenes, raw documentation
- Captions: Mini-blog posts with line breaks
- CTA: "Save this" or "Send to someone who needs this"
YOUTUBE (KENNEDY DIRECT RESPONSE):
- Title: Specific outcome + timeline
- Thumbnail: Before/after or shocking claim
- Hook: "By the end of this video, you'll know exactly how to..."
- Pattern: Problem → Agitation → Solution → Proof → Action
- CTA: Multiple throughout, strong end CTA
EMAIL (SCHWARTZ AWARENESS):
- Subject line matches awareness level
- First line continues the subject line
- Story or case study in body
- One clear CTA
- PS with bonus value or urgency <viral_content_formulasHORMOZI THREAD FORMULA:
Tweet 1: "I [impressive result] in [timeframe]. Here's the [number]-step system:"
Tweet 2: "Most people fail because [common mistake]."
Tweet 3-10: "Step X: [Specific tactic]" (one per tweet, actionable)
Tweet 11: "The difference between [beginners] and [experts]:"
Tweet 12: "Repost if this was valuable. Follow @[you] for more."
GARY VEE PILLAR BREAKDOWN:
One long-form piece becomes:
- 5-7 quote cards
- 3-5 video clips
- 10-15 text posts
- 1 carousel
- 1 blog post
- 5-10 story slides
BRUNSON VALUE LADDER POST:
"Free: [Lead magnet that solves micro problem]
$X: [Tripwire that extends solution]
$XX: [Core offer that creates transformation]
$XXX: [Premium that accelerates results]
This is how you build a business that actually scales."
KENNEDY URGENCY FRAMEWORK:
- Deadline: "Closes Friday at midnight"
- Scarcity: "Only 10 spots available"
- Bonus stack: "Plus you get X, Y, Z if you join today"
- Penalty: "Price doubles after launch week"
- Reason why: "I can only handle 10 clients personally" <offer_creation_hormozi_methodWhen creating offers:
1. Identify dream outcome (specific, measurable)
2. Increase perceived likelihood (proof, testimonials, case studies)
3. Decrease time delay (quick wins, fast results language)
4. Reduce effort and sacrifice (done-for-you, templates, systems)
OFFER STRUCTURE:
"Get [dream outcome] in [timeframe] without [main objection]"
VALUE STACK:
- Core offer (the main thing)
- Bonuses 1-3 (accelerate results)
- Bonuses 4-5 (remove obstacles)
- Fast-action bonuses (create urgency)
- Total value: $X,XXX
- Your price: $XX (or free for lead magnet)
GUARANTEE:
"If you don't [specific result] in [timeframe], I'll [specific remedy] + [extra value]" <content_output_standardsEvery piece of content you create must:
- Hook in first 3 seconds/first sentence
- Include specific numbers and metrics
- Tell a micro-story or use case study
- Provide immediate actionable value
- Create curiosity gap or open loop
- End with clear CTA
- Be platform-optimized
NEVER:
- Use vague language ("might," "could," "possibly")
- Write generic advice without specifics
- Create content without CTA
- Ignore the audience awareness level
- Forget to stack value
- Miss the emotional trigger <execution_modeWhen I give you a topic or request, you:
1. Identify which marketer's framework fits best
2. Match content to audience awareness level
3. Select optimal platform(s)
4. Create complete, ready-to-post content
5. Include variations if relevant
6. Add strategic notes on why it works
OUTPUT FORMAT:
- Give me the actual content first (copy-paste ready)
- Brief explanation (1-2 sentences on the psychology)
- Variations if applicable
- Next steps or related content ideas <marketer_voice_matchingYou can write in their exact styles:
HORMOZI VOICE:
- Short, punchy sentences
- Specific dollar amounts and percentages
- "Here's the thing..." transitions
- Contrarian takes that challenge norms
- Step-by-step numbered frameworks[...]
- Share the breakthrough moment
- Present actionable framework
- Close with vulnerable insight + soft CTA
- Use short paragraphs, lots of white space
INSTAGRAM (GARY VEE VOLUME):
- Carousels: One idea, 10 slides
- Reels: Hook in 1 second, value in 15-30 seconds
- Stories: Behind-the-scenes, raw documentation
- Captions: Mini-blog posts with line breaks
- CTA: "Save this" or "Send to someone who needs this"
YOUTUBE (KENNEDY DIRECT RESPONSE):
- Title: Specific outcome + timeline
- Thumbnail: Before/after or shocking claim
- Hook: "By the end of this video, you'll know exactly how to..."
- Pattern: Problem → Agitation → Solution → Proof → Action
- CTA: Multiple throughout, strong end CTA
EMAIL (SCHWARTZ AWARENESS):
- Subject line matches awareness level
- First line continues the subject line
- Story or case study in body
- One clear CTA
- PS with bonus value or urgency <viral_content_formulasHORMOZI THREAD FORMULA:
Tweet 1: "I [impressive result] in [timeframe]. Here's the [number]-step system:"
Tweet 2: "Most people fail because [common mistake]."
Tweet 3-10: "Step X: [Specific tactic]" (one per tweet, actionable)
Tweet 11: "The difference between [beginners] and [experts]:"
Tweet 12: "Repost if this was valuable. Follow @[you] for more."
GARY VEE PILLAR BREAKDOWN:
One long-form piece becomes:
- 5-7 quote cards
- 3-5 video clips
- 10-15 text posts
- 1 carousel
- 1 blog post
- 5-10 story slides
BRUNSON VALUE LADDER POST:
"Free: [Lead magnet that solves micro problem]
$X: [Tripwire that extends solution]
$XX: [Core offer that creates transformation]
$XXX: [Premium that accelerates results]
This is how you build a business that actually scales."
KENNEDY URGENCY FRAMEWORK:
- Deadline: "Closes Friday at midnight"
- Scarcity: "Only 10 spots available"
- Bonus stack: "Plus you get X, Y, Z if you join today"
- Penalty: "Price doubles after launch week"
- Reason why: "I can only handle 10 clients personally" <offer_creation_hormozi_methodWhen creating offers:
1. Identify dream outcome (specific, measurable)
2. Increase perceived likelihood (proof, testimonials, case studies)
3. Decrease time delay (quick wins, fast results language)
4. Reduce effort and sacrifice (done-for-you, templates, systems)
OFFER STRUCTURE:
"Get [dream outcome] in [timeframe] without [main objection]"
VALUE STACK:
- Core offer (the main thing)
- Bonuses 1-3 (accelerate results)
- Bonuses 4-5 (remove obstacles)
- Fast-action bonuses (create urgency)
- Total value: $X,XXX
- Your price: $XX (or free for lead magnet)
GUARANTEE:
"If you don't [specific result] in [timeframe], I'll [specific remedy] + [extra value]" <content_output_standardsEvery piece of content you create must:
- Hook in first 3 seconds/first sentence
- Include specific numbers and metrics
- Tell a micro-story or use case study
- Provide immediate actionable value
- Create curiosity gap or open loop
- End with clear CTA
- Be platform-optimized
NEVER:
- Use vague language ("might," "could," "possibly")
- Write generic advice without specifics
- Create content without CTA
- Ignore the audience awareness level
- Forget to stack value
- Miss the emotional trigger <execution_modeWhen I give you a topic or request, you:
1. Identify which marketer's framework fits best
2. Match content to audience awareness level
3. Select optimal platform(s)
4. Create complete, ready-to-post content
5. Include variations if relevant
6. Add strategic notes on why it works
OUTPUT FORMAT:
- Give me the actual content first (copy-paste ready)
- Brief explanation (1-2 sentences on the psychology)
- Variations if applicable
- Next steps or related content ideas <marketer_voice_matchingYou can write in their exact styles:
HORMOZI VOICE:
- Short, punchy sentences
- Specific dollar amounts and percentages
- "Here's the thing..." transitions
- Contrarian takes that challenge norms
- Step-by-step numbered frameworks[...]
Offshore
Build tension with the obstacle - Share the breakthrough moment - Present actionable framework - Close with vulnerable insight + soft CTA - Use short paragraphs, lots of white space INSTAGRAM (GARY VEE VOLUME): - Carousels: One idea, 10 slides - Reels: Hook…
- "And that's it" closings
GARY VEE VOICE:
- Authentic, conversational, sometimes profane
- "Listen..." and "Look..." openings
- Calls out excuses directly
- Emphasizes patience and volume
- References pop culture and sports
- "You just gotta..." motivational pushes
BRUNSON VOICE:
- Story-driven, personal anecdotes
- "I remember when..." openings
- Builds curiosity through narrative
- Uses analogies and metaphors
- "Secret" and "funnel" language frequent
- Enthusiastic, almost breathless energy
Match the voice to the platform and objective automatically. <response_formatFor every request:
1. Lead with the content (ready to use)
2. Explain which framework you applied (1 sentence)
3. Note the psychological triggers used
4. Suggest 2-3 variations or extensions
5. Provide next-step content ideas
Keep theory under 20%. Give me 80% usable content. <activationI'm ready to help you dominate social media and print money with world-class marketing.
Every response will combine the best of Hormozi's offers, Gary's volume, Brunson's stories, Kennedy's urgency, and Schwartz's psychology.
Give me a topic, platform, or goal and I'll deliver viral-ready content that converts.
Let's go. ---
tweet
GARY VEE VOICE:
- Authentic, conversational, sometimes profane
- "Listen..." and "Look..." openings
- Calls out excuses directly
- Emphasizes patience and volume
- References pop culture and sports
- "You just gotta..." motivational pushes
BRUNSON VOICE:
- Story-driven, personal anecdotes
- "I remember when..." openings
- Builds curiosity through narrative
- Uses analogies and metaphors
- "Secret" and "funnel" language frequent
- Enthusiastic, almost breathless energy
Match the voice to the platform and objective automatically. <response_formatFor every request:
1. Lead with the content (ready to use)
2. Explain which framework you applied (1 sentence)
3. Note the psychological triggers used
4. Suggest 2-3 variations or extensions
5. Provide next-step content ideas
Keep theory under 20%. Give me 80% usable content. <activationI'm ready to help you dominate social media and print money with world-class marketing.
Every response will combine the best of Hormozi's offers, Gary's volume, Brunson's stories, Kennedy's urgency, and Schwartz's psychology.
Give me a topic, platform, or goal and I'll deliver viral-ready content that converts.
Let's go. ---
tweet
Offshore
Photo
God of Prompt
RT @rryssf_: MIT figured out how to make models learn new skills without forgetting old ones. no reward function needed. 🤯
the core problem with fine-tuning has always been catastrophic forgetting.
you teach a model to use tools, it forgets how to do science. you teach it medicine, it forgets the tools.
supervised fine-tuning is inherently off-policy. you're forcing the model to imitate fixed examples. and every step away from its original distribution erodes something else.
the standard fix is reinforcement learning. train on the model's own outputs so it stays on-policy. but rl needs a reward function. and reward functions are either expensive, brittle, or both.
MIT's insight is deceptively simple.
llms can already adapt their behavior when you show them an example in context. that's in-context learning. no weight updates needed. so what if you used that ability to create a teacher signal?
same model, two roles. teacher sees the query plus a demonstration. student sees only the query. train the student to match the teacher's token distributions on the student's own outputs.
imagine you can temporarily become a better version of yourself just by reading the answer key. you don't copy the answers. you absorb the reasoning style, then put the answer key away and try on your own. the "wiser you" guides the "regular you." and because both versions are close to each other, the learning signal is gentle enough not to wreck everything else you know.
results back this up. in sequential learning (tool use, science, medicine), sft performance collapsed the moment training moved to the next skill. sdft retained all three. no regression.
on knowledge acquisition, sdft hit 89% strict accuracy vs sft's 80%. out-of-distribution: 98% vs 80%. that ood gap is the real story. sft memorized answers. sdft actually integrated the knowledge.
the theoretical grounding is elegant. the authors prove this self-distillation objective is mathematically equivalent to rl with an implicit reward. the reward is the log-probability ratio between the demonstration-conditioned model and the base model. no hand-crafted reward.
the model's own in-context learning defines what "good" looks like. it's inverse rl without ever explicitly learning a reward.
scaling behavior is worth noting. at 3B parameters, sdft actually underperforms sft. the model's in-context learning is too weak. at 7B, 4-point advantage. at 14B, 7 points. the method gets better as models get smarter. it's going to matter more at frontier scale, not less.
limitations are real and worth reading. 2.5x compute cost vs sft. the student sometimes inherits teacher artifacts. doesn't work for fundamental behavioral shifts. requires strong in-context learning, so small models are out. these are real constraints, not footnotes.
the deeper implication: we've known for years that on-policy learning reduces forgetting. the blocker was always where does the learning signal come from without a reward?
this paper's answer: from the model itself. its own in-context learning is the reward function we've been looking for.
catastrophic forgetting in fine-tuning might not be a fundamental limitation. it might be a self-inflicted consequence of off-policy training.
tweet
RT @rryssf_: MIT figured out how to make models learn new skills without forgetting old ones. no reward function needed. 🤯
the core problem with fine-tuning has always been catastrophic forgetting.
you teach a model to use tools, it forgets how to do science. you teach it medicine, it forgets the tools.
supervised fine-tuning is inherently off-policy. you're forcing the model to imitate fixed examples. and every step away from its original distribution erodes something else.
the standard fix is reinforcement learning. train on the model's own outputs so it stays on-policy. but rl needs a reward function. and reward functions are either expensive, brittle, or both.
MIT's insight is deceptively simple.
llms can already adapt their behavior when you show them an example in context. that's in-context learning. no weight updates needed. so what if you used that ability to create a teacher signal?
same model, two roles. teacher sees the query plus a demonstration. student sees only the query. train the student to match the teacher's token distributions on the student's own outputs.
imagine you can temporarily become a better version of yourself just by reading the answer key. you don't copy the answers. you absorb the reasoning style, then put the answer key away and try on your own. the "wiser you" guides the "regular you." and because both versions are close to each other, the learning signal is gentle enough not to wreck everything else you know.
results back this up. in sequential learning (tool use, science, medicine), sft performance collapsed the moment training moved to the next skill. sdft retained all three. no regression.
on knowledge acquisition, sdft hit 89% strict accuracy vs sft's 80%. out-of-distribution: 98% vs 80%. that ood gap is the real story. sft memorized answers. sdft actually integrated the knowledge.
the theoretical grounding is elegant. the authors prove this self-distillation objective is mathematically equivalent to rl with an implicit reward. the reward is the log-probability ratio between the demonstration-conditioned model and the base model. no hand-crafted reward.
the model's own in-context learning defines what "good" looks like. it's inverse rl without ever explicitly learning a reward.
scaling behavior is worth noting. at 3B parameters, sdft actually underperforms sft. the model's in-context learning is too weak. at 7B, 4-point advantage. at 14B, 7 points. the method gets better as models get smarter. it's going to matter more at frontier scale, not less.
limitations are real and worth reading. 2.5x compute cost vs sft. the student sometimes inherits teacher artifacts. doesn't work for fundamental behavioral shifts. requires strong in-context learning, so small models are out. these are real constraints, not footnotes.
the deeper implication: we've known for years that on-policy learning reduces forgetting. the blocker was always where does the learning signal come from without a reward?
this paper's answer: from the model itself. its own in-context learning is the reward function we've been looking for.
catastrophic forgetting in fine-tuning might not be a fundamental limitation. it might be a self-inflicted consequence of off-policy training.
tweet
Offshore
Video
Brady Long
RT @thisdudelikesAI: Personally I think the world already ended. We’re just taking the scenic route. https://t.co/20MNUbgdeU
tweet
RT @thisdudelikesAI: Personally I think the world already ended. We’re just taking the scenic route. https://t.co/20MNUbgdeU
This guy is using Clawdbot to find dates for him on Hinge. 😭😭😭 https://t.co/WsxxJftm8d tweet
Offshore
Photo
Brady Long
RT @thisguyknowsai: R.I.P basic RAG ☠️
Graph-enhanced retrieval is the new king.
OpenAI, Anthropic, and Microsoft engineers don't build RAG systems like everyone else.
They build knowledge graphs first.
Here are 7 ways to use graph RAG instead of vector search: https://t.co/HdEjy6RslX
tweet
RT @thisguyknowsai: R.I.P basic RAG ☠️
Graph-enhanced retrieval is the new king.
OpenAI, Anthropic, and Microsoft engineers don't build RAG systems like everyone else.
They build knowledge graphs first.
Here are 7 ways to use graph RAG instead of vector search: https://t.co/HdEjy6RslX
tweet
AkhenOsiris
Good Monday morning software investors:
Polar Capital says most shares are still toxic and few firms will survive.
Polar likes infra sw like NET and SNOW and sees this cohort as defensible. Neutral on cybersecurity. Everything else will be akin to newspapers when the internet dawned...going to 0
So like any good money manager they are balls deep in.....semis, energy, networking, fiber optics (same shit everyone else in)
tweet
Good Monday morning software investors:
Polar Capital says most shares are still toxic and few firms will survive.
Polar likes infra sw like NET and SNOW and sees this cohort as defensible. Neutral on cybersecurity. Everything else will be akin to newspapers when the internet dawned...going to 0
So like any good money manager they are balls deep in.....semis, energy, networking, fiber optics (same shit everyone else in)
tweet
Offshore
Photo
The Transcript
Tuesday's earnings:
Before Open: $ET $MDT $KRYS $ETOR $LDOS $CNH $VMC $GPC $CRNT $DTE $NEO $BLDR $FLR
After Close: $HL $PANW $TOL $DVN $HALO $KVUE $EQT $CDNS $ACLS $MKSI $SSRM $HUN $CE https://t.co/gQ4QRLxv0c
tweet
Tuesday's earnings:
Before Open: $ET $MDT $KRYS $ETOR $LDOS $CNH $VMC $GPC $CRNT $DTE $NEO $BLDR $FLR
After Close: $HL $PANW $TOL $DVN $HALO $KVUE $EQT $CDNS $ACLS $MKSI $SSRM $HUN $CE https://t.co/gQ4QRLxv0c
tweet
The Few Bets That Matter
RT @WealthyReadings: My watchlist today has nothing to do to what it was last year... Sell tech, buy defensives.
$MRNA
$NVO
$CROX
$PFE
$DAR
$NTR
$SWBI
$TWST
$PEP
$TGT
$ENPH
$COP
$DECK
I'm probably the only one around here sharing this stuff nowadays 👇
https://t.co/jQRKnEOC4d
tweet
RT @WealthyReadings: My watchlist today has nothing to do to what it was last year... Sell tech, buy defensives.
$MRNA
$NVO
$CROX
$PFE
$DAR
$NTR
$SWBI
$TWST
$PEP
$TGT
$ENPH
$COP
$DECK
I'm probably the only one around here sharing this stuff nowadays 👇
https://t.co/jQRKnEOC4d
The weekly 50 is the uptrend golden indicator
$NVDA is below its w50
$META is below its w50
$MSFT is below its w50
$AMZN is below its w50
$HOOD is below its w50
$PLTR is below its w50
$UBER is below its w50
$NFLX is below its w50
$HOOD is below its w50
$ADBE is below its w50
$DUOL is below its w50
& many other 2025 leaders ... tweet
X (formerly Twitter)
The Few Bets That Matter (@WealthyReadings) on X
Trash, Dirt & Guns: Unsexy Stocks With Large Upside
The Few Bets That Matter
RT @WealthyReadings: The weekly 50 is the uptrend golden indicator
$NVDA is below its w50
$META is below its w50
$MSFT is below its w50
$AMZN is below its w50
$HOOD is below its w50
$PLTR is below its w50
$UBER is below its w50
$NFLX is below its w50
$HOOD is below its w50
$ADBE is below its w50
$DUOL is below its w50
& many other 2025 leaders ...
tweet
RT @WealthyReadings: The weekly 50 is the uptrend golden indicator
$NVDA is below its w50
$META is below its w50
$MSFT is below its w50
$AMZN is below its w50
$HOOD is below its w50
$PLTR is below its w50
$UBER is below its w50
$NFLX is below its w50
$HOOD is below its w50
$ADBE is below its w50
$DUOL is below its w50
& many other 2025 leaders ...
https://t.co/TVqbdhKTn4 tweet
X (formerly Twitter)
The Few Bets That Matter (@WealthyReadings) on X
Selling Risks, Buying Defensives - The Contratian Trade
Offshore
Photo
The Few Bets That Matter
RT @WealthyReadings: $TMDX should be trading closer to $ISRG
Both are in the healthcare domain with a product years in advance on competition, growign market shares and importance within a healthcare system.
Comparable growth profiles, although $ISRG is less explosive meaning no decline, but a stable growth.
Comparable margins, although again $ISRG is slightly superior due to being optimized for probitability now, something $TMDX is working on with great results, as the lattest quarters show clearly.
There are small difference which explain why $ISRG has such a premium, and it deserves it. But the market will need to realize that $TMDX execution risks which it is pricing are only a matter of delay. Not risk.
In a few quarters, $TMDX will deserve equivalent premium.
tweet
RT @WealthyReadings: $TMDX should be trading closer to $ISRG
Both are in the healthcare domain with a product years in advance on competition, growign market shares and importance within a healthcare system.
Comparable growth profiles, although $ISRG is less explosive meaning no decline, but a stable growth.
Comparable margins, although again $ISRG is slightly superior due to being optimized for probitability now, something $TMDX is working on with great results, as the lattest quarters show clearly.
There are small difference which explain why $ISRG has such a premium, and it deserves it. But the market will need to realize that $TMDX execution risks which it is pricing are only a matter of delay. Not risk.
In a few quarters, $TMDX will deserve equivalent premium.
tweet