Offshore
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Benjamin Hernandez😎
Halted stock just resumed—momentum building fast. My WhatsApp is analyzing the tape live, calling the breakout or fade in real-time. This is where fortunes flip in minutes. We're positioned
Get in live 🔥 https://t.co/71FIJId47G
Text "Hi" immediately
$GME $HOOD $SOFI $PLTR $TSM
tweet
Halted stock just resumed—momentum building fast. My WhatsApp is analyzing the tape live, calling the breakout or fade in real-time. This is where fortunes flip in minutes. We're positioned
Get in live 🔥 https://t.co/71FIJId47G
Text "Hi" immediately
$GME $HOOD $SOFI $PLTR $TSM
📉 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
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DAIR.AI
RT @omarsar0: Another great paper if you are building with coding agents.
(great insights on this one; bookmark it)
This reminds be a bit of the recently released agent teams in Claude Code.
Why it matters?
Single-agent coding systems have hit a ceiling most devs don't talk about.
The default approach to building AI coding agents today is a single model responsible for everything: understanding issues, navigating code, writing patches, and verifying correctness.
But real software engineering has never been a solo activity.
This new research introduces Agyn, an open-source multi-agent platform that models software engineering as a team-based organizational process rather than a monolithic task.
The system configures a team of four specialized agents: a manager, researcher, engineer, and reviewer. Each operates within its own isolated sandbox with role-specific tools, prompts, and language model configurations. The manager agent coordinates dynamically based on intermediate outcomes rather than following a fixed pipeline.
What makes the design interesting?
Different agents use different models depending on their role. The manager and researcher run on GPT-5 for stronger reasoning and broader context. The engineer and reviewer use GPT-5-Codex, a smaller code-specialized model optimized for iterative implementation and debugging. This mirrors how real teams allocate resources based on task requirements.
The workflow follows a GitHub-native process. Agents analyze issues, create pull requests, conduct inline code reviews, and iterate through revision cycles until the reviewer explicitly approves. No human intervention at any point. The number of steps isn't predetermined. It emerges from task complexity.
Here is one notable finding:
Starting agents from empty environments proved more effective than preconfigured setups. Agents use Nix to install dependencies as needed, avoiding implicit assumptions that conflict with project-specific requirements. When command outputs exceed 50,000 tokens, they're automatically redirected to files rather than overwhelming the model context.
On SWE-bench 500, the system resolves 72.4% of tasks, outperforming single-agent baselines using comparable model configurations. OpenHands + GPT-5 achieves 71.8%, and mini-SWE-agent + GPT-5 reaches 65.0%. Importantly, the system was designed for production use and was not tuned for the benchmark.
Organizational structure and coordination design can be as important for autonomous software engineering as improvements in underlying models. Teams of specialized agents with clear roles, isolated workspaces, and structured communication outperform monolithic approaches even with comparable compute.
Paper: https://t.co/YVX2OZCxFq
Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX
tweet
RT @omarsar0: Another great paper if you are building with coding agents.
(great insights on this one; bookmark it)
This reminds be a bit of the recently released agent teams in Claude Code.
Why it matters?
Single-agent coding systems have hit a ceiling most devs don't talk about.
The default approach to building AI coding agents today is a single model responsible for everything: understanding issues, navigating code, writing patches, and verifying correctness.
But real software engineering has never been a solo activity.
This new research introduces Agyn, an open-source multi-agent platform that models software engineering as a team-based organizational process rather than a monolithic task.
The system configures a team of four specialized agents: a manager, researcher, engineer, and reviewer. Each operates within its own isolated sandbox with role-specific tools, prompts, and language model configurations. The manager agent coordinates dynamically based on intermediate outcomes rather than following a fixed pipeline.
What makes the design interesting?
Different agents use different models depending on their role. The manager and researcher run on GPT-5 for stronger reasoning and broader context. The engineer and reviewer use GPT-5-Codex, a smaller code-specialized model optimized for iterative implementation and debugging. This mirrors how real teams allocate resources based on task requirements.
The workflow follows a GitHub-native process. Agents analyze issues, create pull requests, conduct inline code reviews, and iterate through revision cycles until the reviewer explicitly approves. No human intervention at any point. The number of steps isn't predetermined. It emerges from task complexity.
Here is one notable finding:
Starting agents from empty environments proved more effective than preconfigured setups. Agents use Nix to install dependencies as needed, avoiding implicit assumptions that conflict with project-specific requirements. When command outputs exceed 50,000 tokens, they're automatically redirected to files rather than overwhelming the model context.
On SWE-bench 500, the system resolves 72.4% of tasks, outperforming single-agent baselines using comparable model configurations. OpenHands + GPT-5 achieves 71.8%, and mini-SWE-agent + GPT-5 reaches 65.0%. Importantly, the system was designed for production use and was not tuned for the benchmark.
Organizational structure and coordination design can be as important for autonomous software engineering as improvements in underlying models. Teams of specialized agents with clear roles, isolated workspaces, and structured communication outperform monolithic approaches even with comparable compute.
Paper: https://t.co/YVX2OZCxFq
Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX
tweet
Offshore
Photo
The Few Bets That Matter
This feels like a top signal imo.
$GOOG management is extremely smart to lock in cheap liquidity for their buildouts with almost no consequences due to the timeframes.
But 100-year bonds makes zero sense and buying them even less, even for a company like $GOOG.
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This feels like a top signal imo.
$GOOG management is extremely smart to lock in cheap liquidity for their buildouts with almost no consequences due to the timeframes.
But 100-year bonds makes zero sense and buying them even less, even for a company like $GOOG.
The 100 year bond is back in the tech world https://t.co/UGKB36SJKg tweet
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
Photo
God of Prompt
RT @godofprompt: I've written 500 articles, 23 whitepapers, and 3 ebooks using Claude over 2 years, and these 10 prompts are the ONLY ones I actually use anymore because they handle 90% of professional writing better than any human editor I've worked with and cost me $0.02 per 1000 words: 👇 https://t.co/Yx6MCNdLbr
tweet
RT @godofprompt: I've written 500 articles, 23 whitepapers, and 3 ebooks using Claude over 2 years, and these 10 prompts are the ONLY ones I actually use anymore because they handle 90% of professional writing better than any human editor I've worked with and cost me $0.02 per 1000 words: 👇 https://t.co/Yx6MCNdLbr
tweet
Offshore
Video
The Transcript
Google after seeing that its 100-year bonds are oversubscribed: https://t.co/EcGFys1ZDJ
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Google after seeing that its 100-year bonds are oversubscribed: https://t.co/EcGFys1ZDJ
tweet
Offshore
Photo
The Transcript
Coca-Cola North America segment:
"Operating income declined 65% for the quarter, driven by items impacting comparability, including a non-cash impairment charge of $960M related to the BODYARMOR trademark"
$KO https://t.co/ebJNy2YHg3
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Coca-Cola North America segment:
"Operating income declined 65% for the quarter, driven by items impacting comparability, including a non-cash impairment charge of $960M related to the BODYARMOR trademark"
$KO https://t.co/ebJNy2YHg3
tweet
Offshore
Photo
Benjamin Hernandez😎
$JZXN pushed past $2.31 and rewarded patience with an +70% run. Trend control remains with buyers and continuation is on radar.
Preparing the next signal now.
Get access: ✅https://t.co/71FIJId47G
Type “Guide” for complete insights
$SOFI $HOOD $PLTR $MU
tweet
$JZXN pushed past $2.31 and rewarded patience with an +70% run. Trend control remains with buyers and continuation is on radar.
Preparing the next signal now.
Get access: ✅https://t.co/71FIJId47G
Type “Guide” for complete insights
$SOFI $HOOD $PLTR $MU
📉 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
Javier Blas
RT @michaelh992: Trump says he is considering deploying a second aircraft carrier to the Middle East
tweet
RT @michaelh992: Trump says he is considering deploying a second aircraft carrier to the Middle East
tweet
Offshore
Photo
The Transcript
RT @TheTranscript_: $UBER CEO @dkhos used the prepared remarks in today's earnings call to handle some misconceptions on Autonomous Vehicles
—Myth: AV growth will be zero-sum.
Reality: AVs add supply and expand the ridesharing market.
—Myth: San Francisco trends apply everywhere.
Reality: SF is an outlier & most cities have very different demand and regulatory dynamics.
—Myth: AV-only fleets can achieve high utilization at scale.
Reality: Hybrid networks of AVs and human drivers deliver higher utilization, lower ETAs, and better reliability.
—Myth: AVs only need a few large cities to capture most profits.
Reality: Much of U.S. trip volume and profits sit outside top metros and will remain human-led for years.
—Myth: AVs will soon handle all trips.
Reality: Edge cases, weather, infrastructure, and regulation still limit universal AV coverage.
tweet
RT @TheTranscript_: $UBER CEO @dkhos used the prepared remarks in today's earnings call to handle some misconceptions on Autonomous Vehicles
—Myth: AV growth will be zero-sum.
Reality: AVs add supply and expand the ridesharing market.
—Myth: San Francisco trends apply everywhere.
Reality: SF is an outlier & most cities have very different demand and regulatory dynamics.
—Myth: AV-only fleets can achieve high utilization at scale.
Reality: Hybrid networks of AVs and human drivers deliver higher utilization, lower ETAs, and better reliability.
—Myth: AVs only need a few large cities to capture most profits.
Reality: Much of U.S. trip volume and profits sit outside top metros and will remain human-led for years.
—Myth: AVs will soon handle all trips.
Reality: Edge cases, weather, infrastructure, and regulation still limit universal AV coverage.
tweet