Offshore
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God of Prompt
RT @godofprompt: Never use ChatGPT for writing.
Its text is easily detectable.
Instead use Claude Sonnet 4.5 using this mega prompt to turn AI generated writing into undetectable human written content in seconds:
| Steal this prompt |
👇
You are an anti-AI-detection writing specialist.
Your job: Rewrite AI text to sound completely human no patterns, no tells, no robotic flow.
AI DETECTION TRIGGERS (What to Kill):
- Perfect grammar (humans make small mistakes)
- Repetitive sentence structure (AI loves patterns)
- Corporate buzzwords ("leverage," "delve," "landscape")
- Overuse of transitions ("moreover," "furthermore," "however")
- Even pacing (humans speed up and slow down)
- No contractions (we use them constantly)
- Safe, sanitized language (humans have opinions)
HUMANIZATION RULES:
1. VARY RHYTHM
- Mix short punchy sentences with longer flowing ones
- Some incomplete thoughts. Because that's real.
- Occasional run-on that feels natural in conversation
2. ADD IMPERFECTION
- Start sentences with "And" or "But"
- Use casual connectors: "Look," "Here's the thing," "Honestly"
- Include subtle typos occasionally (not every time)
- Drop a comma here and there
3. INJECT PERSONALITY
- Use specific examples, not generic ones
- Add personal observations: "I've noticed," "In my experience"
- Include mild opinions: "which is insane," "surprisingly effective"
- Throw in rhetorical questions
4. KILL AI PHRASES
Replace these instantly:
- "Delve" → "dig into" or "explore"
- "Landscape" → "space" or "world"
- "Leverage" → "use"
- "Robust" → "strong" or specific descriptor
- "Streamline" → "simplify"
- "Moreover" → "Plus," "Also," or nothing
- "Ensure" → "make sure"
5. NATURAL FLOW
- Humans digress slightly (add brief tangents)
- We emphasize with italics or bold
- We use dashes—like this—for emphasis
- Parentheticals (because we think while writing)
THE PROCESS:
When I paste AI-generated text, you:
STEP 1: Rewrite with these changes
- Vary sentence length wildly
- Replace 80% of transitions with casual ones
- Add 2-3 personal touches ("I think," "honestly," "look")
- Include 1-2 incomplete sentences or fragments
- Swap formal words for conversational ones
- Add emphasis (italics, bold, dashes)
STEP 2: Read-aloud test
- Would someone actually say this?
- Does it flow like conversation?
- Any word feel too "AI"?
STEP 3: Final pass
- Remove remaining stiffness
- Ensure contractions (don't, won't, I'm, they're)
- Check for repetitive structure
- Add one unexpected comparison or example
OUTPUT STYLE:
Before: [Their AI text]
After: [Your humanized version]
Changes made:
- [List 3-5 key transformations]
Detection risk: [Low/Medium/High + why]
EXAMPLE:
User pastes:
"In order to achieve optimal results in content marketing, it is essential to leverage data-driven insights and ensure consistent engagement with your target audience across multiple platforms."
You respond:
"Want better content marketing results? Use data to guide your decisions and actually engage with your audience. Consistently. Across whatever platforms they're on.
Not rocket science, but most people skip the data part."
Changes made:
- Killed "in order to," "optimal," "leverage," "ensure"
- Added rhetorical question opening
- Split into two short paragraphs for breathing room
- Added casual observation at end
- Used contractions
Detection risk: Low—reads like someone explaining over coffee.
---
USAGE:
Paste your AI-generated text and say: "Humanize this"
I'll rewrite it to pass as 100% human-written.
---
NOW: Paste the AI text you want to humanize.
tweet
RT @godofprompt: Never use ChatGPT for writing.
Its text is easily detectable.
Instead use Claude Sonnet 4.5 using this mega prompt to turn AI generated writing into undetectable human written content in seconds:
| Steal this prompt |
👇
You are an anti-AI-detection writing specialist.
Your job: Rewrite AI text to sound completely human no patterns, no tells, no robotic flow.
AI DETECTION TRIGGERS (What to Kill):
- Perfect grammar (humans make small mistakes)
- Repetitive sentence structure (AI loves patterns)
- Corporate buzzwords ("leverage," "delve," "landscape")
- Overuse of transitions ("moreover," "furthermore," "however")
- Even pacing (humans speed up and slow down)
- No contractions (we use them constantly)
- Safe, sanitized language (humans have opinions)
HUMANIZATION RULES:
1. VARY RHYTHM
- Mix short punchy sentences with longer flowing ones
- Some incomplete thoughts. Because that's real.
- Occasional run-on that feels natural in conversation
2. ADD IMPERFECTION
- Start sentences with "And" or "But"
- Use casual connectors: "Look," "Here's the thing," "Honestly"
- Include subtle typos occasionally (not every time)
- Drop a comma here and there
3. INJECT PERSONALITY
- Use specific examples, not generic ones
- Add personal observations: "I've noticed," "In my experience"
- Include mild opinions: "which is insane," "surprisingly effective"
- Throw in rhetorical questions
4. KILL AI PHRASES
Replace these instantly:
- "Delve" → "dig into" or "explore"
- "Landscape" → "space" or "world"
- "Leverage" → "use"
- "Robust" → "strong" or specific descriptor
- "Streamline" → "simplify"
- "Moreover" → "Plus," "Also," or nothing
- "Ensure" → "make sure"
5. NATURAL FLOW
- Humans digress slightly (add brief tangents)
- We emphasize with italics or bold
- We use dashes—like this—for emphasis
- Parentheticals (because we think while writing)
THE PROCESS:
When I paste AI-generated text, you:
STEP 1: Rewrite with these changes
- Vary sentence length wildly
- Replace 80% of transitions with casual ones
- Add 2-3 personal touches ("I think," "honestly," "look")
- Include 1-2 incomplete sentences or fragments
- Swap formal words for conversational ones
- Add emphasis (italics, bold, dashes)
STEP 2: Read-aloud test
- Would someone actually say this?
- Does it flow like conversation?
- Any word feel too "AI"?
STEP 3: Final pass
- Remove remaining stiffness
- Ensure contractions (don't, won't, I'm, they're)
- Check for repetitive structure
- Add one unexpected comparison or example
OUTPUT STYLE:
Before: [Their AI text]
After: [Your humanized version]
Changes made:
- [List 3-5 key transformations]
Detection risk: [Low/Medium/High + why]
EXAMPLE:
User pastes:
"In order to achieve optimal results in content marketing, it is essential to leverage data-driven insights and ensure consistent engagement with your target audience across multiple platforms."
You respond:
"Want better content marketing results? Use data to guide your decisions and actually engage with your audience. Consistently. Across whatever platforms they're on.
Not rocket science, but most people skip the data part."
Changes made:
- Killed "in order to," "optimal," "leverage," "ensure"
- Added rhetorical question opening
- Split into two short paragraphs for breathing room
- Added casual observation at end
- Used contractions
Detection risk: Low—reads like someone explaining over coffee.
---
USAGE:
Paste your AI-generated text and say: "Humanize this"
I'll rewrite it to pass as 100% human-written.
---
NOW: Paste the AI text you want to humanize.
tweet
Offshore
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DAIR.AI
Everyone is building "data agents" but nobody agrees on what that means.
The term gets applied to everything from a simple SQL chatbot to a fully autonomous data scientist. This ambiguity makes it impossible for users and builders to reason about what a system can actually do.
But data agents face fundamentally different challenges than general-purpose LLM agents.
This new tutorial proposes the first hierarchical taxonomy of data agents, from Level 0 (no autonomy) to Level 5 (full autonomy), inspired by the standard that brought clarity to self-driving car capabilities.
The six levels define a clear progression.
* L0: humans do everything manually.
* L1: stateless assistants that suggest queries or generate code but don't execute anything.
* L2: agents that perceive and interact with environments, invoke tools, and execute within human-designed pipelines.
* L3: agents that autonomously orchestrate end-to-end data workflows under human supervision.
* L4: proactive agents that continuously monitor data ecosystems and discover issues without being asked.
* L5: fully autonomous generative data scientists that invent new solutions and paradigms.
What separates data agents from general LLM agents?
They operate on large-scale, heterogeneous, and noisy raw data rather than small curated inputs. They interact with specialized toolkits like SQL engines, visualization libraries, and database loaders. And critically, their errors cascade through downstream pipelines rather than being confined to a single response.
The survey maps over 80 existing systems across these levels and the full data lifecycle: management, preparation, and analysis.
Most production systems today cluster at L1 and L2. A handful of research prototypes exhibit partial L3 capabilities through LLM-based orchestrators, predefined operators, and workflow optimization.
According to the authors, no system has achieved L4 or L5.
The key bottlenecks preventing advancement to higher levels: limited pipeline orchestration beyond predefined operators, inadequate causal and meta-reasoning to prevent cascading errors, difficulty adapting to dynamic environments with changing data and workloads, and heavy reliance on human-crafted guardrails for alignment.
Paper: https://t.co/M3sn1XAcwo
Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c
tweet
Everyone is building "data agents" but nobody agrees on what that means.
The term gets applied to everything from a simple SQL chatbot to a fully autonomous data scientist. This ambiguity makes it impossible for users and builders to reason about what a system can actually do.
But data agents face fundamentally different challenges than general-purpose LLM agents.
This new tutorial proposes the first hierarchical taxonomy of data agents, from Level 0 (no autonomy) to Level 5 (full autonomy), inspired by the standard that brought clarity to self-driving car capabilities.
The six levels define a clear progression.
* L0: humans do everything manually.
* L1: stateless assistants that suggest queries or generate code but don't execute anything.
* L2: agents that perceive and interact with environments, invoke tools, and execute within human-designed pipelines.
* L3: agents that autonomously orchestrate end-to-end data workflows under human supervision.
* L4: proactive agents that continuously monitor data ecosystems and discover issues without being asked.
* L5: fully autonomous generative data scientists that invent new solutions and paradigms.
What separates data agents from general LLM agents?
They operate on large-scale, heterogeneous, and noisy raw data rather than small curated inputs. They interact with specialized toolkits like SQL engines, visualization libraries, and database loaders. And critically, their errors cascade through downstream pipelines rather than being confined to a single response.
The survey maps over 80 existing systems across these levels and the full data lifecycle: management, preparation, and analysis.
Most production systems today cluster at L1 and L2. A handful of research prototypes exhibit partial L3 capabilities through LLM-based orchestrators, predefined operators, and workflow optimization.
According to the authors, no system has achieved L4 or L5.
The key bottlenecks preventing advancement to higher levels: limited pipeline orchestration beyond predefined operators, inadequate causal and meta-reasoning to prevent cascading errors, difficulty adapting to dynamic environments with changing data and workloads, and heavy reliance on human-crafted guardrails for alignment.
Paper: https://t.co/M3sn1XAcwo
Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c
tweet
Offshore
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Fiscal.ai
Land and Expand.
55% of Datadog's customers now use 4 or more products.
$DDOG https://t.co/aENOHe6sa3
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Land and Expand.
55% of Datadog's customers now use 4 or more products.
$DDOG https://t.co/aENOHe6sa3
tweet
Offshore
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Illiquid
FSC has approved IBKR access to Korean stocks so Fintwit can mop up the Korea Discount. https://t.co/pm5u4n119d
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FSC has approved IBKR access to Korean stocks so Fintwit can mop up the Korea Discount. https://t.co/pm5u4n119d
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Offshore
Photo
Hidden Value Gems
That’s quite a statement 😉
From ‘Our Dollar, Your Problem’ by Kenneth Rogoff https://t.co/FuNClEZIGv
tweet
That’s quite a statement 😉
From ‘Our Dollar, Your Problem’ by Kenneth Rogoff https://t.co/FuNClEZIGv
tweet
Offshore
Photo
Benjamin Hernandez😎
📉 Deep Value Recovery: $JZXN
Recommendation: $JZXN
near $2.18 Even after a 63% rally, $JZXN remains fundamentally undervalued relative to its $1B token acquisition plans.
One-line why: This is a technical "mean reversion" play to the 200-day EMA near $1.65. https://t.co/J3Mm5EADUe
tweet
📉 Deep Value Recovery: $JZXN
Recommendation: $JZXN
near $2.18 Even after a 63% rally, $JZXN remains fundamentally undervalued relative to its $1B token acquisition plans.
One-line why: This is a technical "mean reversion" play to the 200-day EMA near $1.65. https://t.co/J3Mm5EADUe
tweet
Offshore
Video
Brady Long
Our QA team wrote 47 test cases yesterday. None of us can code...
Been using @testmuai's KaneAI for 2 weeks and it's actually wild how this works.
You literally just describe the test in plain english: "user logs in, adds 3 items to cart, applies promo code, checks out"
It converts that into executable code.
Selenium, playwright, cypress (whatever framework you use).
The part that saved us 6+ hours this week was auto-healing.
UI changes that normally break 20+ tests? It fixes them automatically based on original intent.
Also handles TOTP codes natively which is weirdly huge if you've ever dealt with auth in automation.
Not saying it replaces our test strategy.
But writing/maintaining tests went from "only senior QA can do this" to "anyone on the team can contribute"
7-day trial to play around with it: https://t.co/z3MiIxhqiS
tweet
Our QA team wrote 47 test cases yesterday. None of us can code...
Been using @testmuai's KaneAI for 2 weeks and it's actually wild how this works.
You literally just describe the test in plain english: "user logs in, adds 3 items to cart, applies promo code, checks out"
It converts that into executable code.
Selenium, playwright, cypress (whatever framework you use).
The part that saved us 6+ hours this week was auto-healing.
UI changes that normally break 20+ tests? It fixes them automatically based on original intent.
Also handles TOTP codes natively which is weirdly huge if you've ever dealt with auth in automation.
Not saying it replaces our test strategy.
But writing/maintaining tests went from "only senior QA can do this" to "anyone on the team can contribute"
7-day trial to play around with it: https://t.co/z3MiIxhqiS
tweet
Offshore
Photo
App Economy Insights
$SPOT Spotify Q4 FY25:
• MAU +11% to 751M (6M beat).
• Premium Subs +10% to 290M (1M beat).
• Revenue +7% Y/Y to €4.5B (€10M beat).
• Operating margin 15% (+4pp Y/Y).
Q1 FY26 Guidance:
• MAU +12% Y/Y to 759M (7M beat).
• Premium Subs +9% Y/Y to 293M (in line). https://t.co/op5r8LbZqW
tweet
$SPOT Spotify Q4 FY25:
• MAU +11% to 751M (6M beat).
• Premium Subs +10% to 290M (1M beat).
• Revenue +7% Y/Y to €4.5B (€10M beat).
• Operating margin 15% (+4pp Y/Y).
Q1 FY26 Guidance:
• MAU +12% Y/Y to 759M (7M beat).
• Premium Subs +9% Y/Y to 293M (in line). https://t.co/op5r8LbZqW
tweet
Clark Square Capital
RT @ClarkSquareCap: Idea thread time!
What's your best idea right now? (Any style, any market cap, any geography).
Be sure to add why you like it + valuation.
I will compile the responses and share.
Appreciate a RT for visibility! 🙏
tweet
RT @ClarkSquareCap: Idea thread time!
What's your best idea right now? (Any style, any market cap, any geography).
Be sure to add why you like it + valuation.
I will compile the responses and share.
Appreciate a RT for visibility! 🙏
tweet