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Claude Opus 4.6 is so strong at financial analysis, including crunching data and reading SEC filings, that after it launched, financial data stocks sold off, with FactSet dropping nearly 10%. Claude Opus 4.6 can analyze company data, filings, and market info to produce deep financial analysis in hours instead...

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Rohan Paul

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465,744 görüntüleme • 8 ay önce •via X (Twitter)

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this is certified f*cking gold for anyone paying for Claude Sonnet 5.5 costs half of what Opus 5.5 does on real office work across 44 jobs it scores 1844. Opus scores 1846 on coding it went from 10.3% to 70.6% in one version and passed Opus on the way most of what you pay Opus for, Sonnet 5.5 now does at half the price here's how to split the work: • give it to Sonnet 5.5 > bug fixes and well-scoped coding tasks > decks built from your own slide template > spreadsheets and polished documents > UI polish, it has a real eye for design > fast back-and-forth, it runs 30%+ faster • keep it for Opus 5.5 > long, open-ended work with judgment calls for hours > anything where one wrong assumption costs you a day • the setting that saves the most > Medium is the default in the Claude apps and Claude Code > on Low or Medium it beats Sonnet 5's best score for a tenth of the cost > raise effort only when the first answer falls short • the price > $2 in and $10 out, against $4 and $20 for Opus > cache reads $0.20 on both • check before you switch > if you ran Sonnet with thinking off, move to the new between_tools setting first whoever routes the weekly grunt work to Sonnet 5.5 and saves Opus for the hard calls ships more on half the budget, and that's the person who gets noticed
0:24

this is certified f*cking gold for anyone paying for Claude Sonnet 5.5 costs half of what Opus 5.5 does on real office work across 44 jobs it scores 1844. Opus scores 1846 on coding it went from 10.3% to 70.6% in one version and passed Opus on the way most of what you pay Opus for, Sonnet 5.5 now does at half the price here's how to split the work: • give it to Sonnet 5.5 > bug fixes and well-scoped coding tasks > decks built from your own slide template > spreadsheets and polished documents > UI polish, it has a real eye for design > fast back-and-forth, it runs 30%+ faster • keep it for Opus 5.5 > long, open-ended work with judgment calls for hours > anything where one wrong assumption costs you a day • the setting that saves the most > Medium is the default in the Claude apps and Claude Code > on Low or Medium it beats Sonnet 5's best score for a tenth of the cost > raise effort only when the first answer falls short • the price > $2 in and $10 out, against $4 and $20 for Opus > cache reads $0.20 on both • check before you switch > if you ran Sonnet with thinking off, move to the new between_tools setting first whoever routes the weekly grunt work to Sonnet 5.5 and saves Opus for the hard calls ships more on half the budget, and that's the person who gets noticed

Annatar.md

19,513 görüntüleme • 13 gün önce

Bloomberg sells Wall Street a financial command center that costs $32,000 a year. So, a guy named Didier built a free alternative for ordinary investors like you and me. It is called OpenBB. Open it and you get a customizable financial workspace pulling market data, company fundamentals, filings, earnings transcripts, estimates, news, and economic indicators into one screen. > Stocks and ETFs > Options and futures > Forex and crypto > Financial statements > SEC filings > Global economic data Turn any dataset into interactive charts, tables, watchlists, and dashboards. Then ask its built-in AI Copilot to compare companies, explain price movements, summarize filings, or find patterns hidden inside the numbers. It reads the data already sitting on your dashboard and cites the exact sources behind its answers. You can also connect your own files, APIs, databases, research models, and custom AI agents. The underlying Open Data Platform is open source, has more than 70,000 GitHub stars, and installs with one command: pip install openbb The Community workspace is free for individual investors. Wall Street pays nearly $32,000 a year for a financial command center. OpenBB puts one in your browser.
0:33

Bloomberg sells Wall Street a financial command center that costs $32,000 a year. So, a guy named Didier built a free alternative for ordinary investors like you and me. It is called OpenBB. Open it and you get a customizable financial workspace pulling market data, company fundamentals, filings, earnings transcripts, estimates, news, and economic indicators into one screen. > Stocks and ETFs > Options and futures > Forex and crypto > Financial statements > SEC filings > Global economic data Turn any dataset into interactive charts, tables, watchlists, and dashboards. Then ask its built-in AI Copilot to compare companies, explain price movements, summarize filings, or find patterns hidden inside the numbers. It reads the data already sitting on your dashboard and cites the exact sources behind its answers. You can also connect your own files, APIs, databases, research models, and custom AI agents. The underlying Open Data Platform is open source, has more than 70,000 GitHub stars, and installs with one command: pip install openbb The Community workspace is free for individual investors. Wall Street pays nearly $32,000 a year for a financial command center. OpenBB puts one in your browser.

Ihtesham Ali

16,680 görüntüleme • 2 ay önce

In our latest Box AI Enterprise Eval, we tested Paul Jankura’s Claude 4 Sonnet and Opus models, now integrated into Box AI, across enterprise Q&A tasks, technical workflows, and advanced coding scenarios—revealing major advancements in developer productivity and content intelligence. AI-assisted coding and development just reached a new milestone! Here's what we discovered: Claude 4 significantly improves understanding, generating, and debugging code across multiple programming languages. Developers can: ↳ Accelerate code generation ↳ Improve debugging ↳ Enhance technical documentation ↳ Build smarter AI agents 👉 Automating Financial Analysis with Code Generation: We evaluated Claude 4 by using the Box AI API to analyze ten complex 10-K financial reports. Claude 4 dynamically generated Python code to fetch file IDs from a Box folder, automating data extraction. Within two minutes, it accurately extracted key company data such as revenues, metrics, and highlights—demonstrating its potential to streamline demanding analytical tasks. 👉 Understanding Enterprise Content: Our evaluation confirms Claude 4 maintains strong performance on enterprise Q&A tasks, effectively extracting precise details from single documents and reliably synthesizing information across multiple sources. This ensures seamless integration of structured and unstructured data alongside powerful coding capabilities. 🔓 Developer-Centric Use Cases Unlocked: Organizations can leverage Claude 4 within Box AI to: ↳ Create custom engineering agents referencing technical documents stored in Box, pulling real-time data from Jira, or finding solutions on Stack Overflow. ↳ Build intelligent technical support bots capable of analyzing user-provided code snippets against internal manuals. ↳ Automate secure code reviews by evaluating repository code (stored in Box) against security policies. ↳ Efficiently migrate legacy systems by translating old codebases into modern languages or platforms. Ready to empower your developers and accelerate innovation? To explore Claude 4 Sonnet and Opus through Box AI Studio and APIs, contact us at ailabs@box.com and request early access today! Learn more:
4:25

In our latest Box AI Enterprise Eval, we tested Paul Jankura’s Claude 4 Sonnet and Opus models, now integrated into Box AI, across enterprise Q&A tasks, technical workflows, and advanced coding scenarios—revealing major advancements in developer productivity and content intelligence. AI-assisted coding and development just reached a new milestone! Here's what we discovered: Claude 4 significantly improves understanding, generating, and debugging code across multiple programming languages. Developers can: ↳ Accelerate code generation ↳ Improve debugging ↳ Enhance technical documentation ↳ Build smarter AI agents 👉 Automating Financial Analysis with Code Generation: We evaluated Claude 4 by using the Box AI API to analyze ten complex 10-K financial reports. Claude 4 dynamically generated Python code to fetch file IDs from a Box folder, automating data extraction. Within two minutes, it accurately extracted key company data such as revenues, metrics, and highlights—demonstrating its potential to streamline demanding analytical tasks. 👉 Understanding Enterprise Content: Our evaluation confirms Claude 4 maintains strong performance on enterprise Q&A tasks, effectively extracting precise details from single documents and reliably synthesizing information across multiple sources. This ensures seamless integration of structured and unstructured data alongside powerful coding capabilities. 🔓 Developer-Centric Use Cases Unlocked: Organizations can leverage Claude 4 within Box AI to: ↳ Create custom engineering agents referencing technical documents stored in Box, pulling real-time data from Jira, or finding solutions on Stack Overflow. ↳ Build intelligent technical support bots capable of analyzing user-provided code snippets against internal manuals. ↳ Automate secure code reviews by evaluating repository code (stored in Box) against security policies. ↳ Efficiently migrate legacy systems by translating old codebases into modern languages or platforms. Ready to empower your developers and accelerate innovation? To explore Claude 4 Sonnet and Opus through Box AI Studio and APIs, contact us at [email protected] and request early access today! Learn more:

Box

285,689 görüntüleme • 1 yıl önce

Today, we’re pushing a major update to Edison Analysis, our data analysis agent, which is tuned for scientific research and SOTA across data analysis benchmarks. In contrast to Kosmos, which runs for 6-12 hours and produces tens of thousands of lines of code, Edison Analysis runs for seconds to minutes and is best for specific, well-defined computational tasks. It is available both on our platform under the Analysis tab, and via API, and costs only one credit per run, so it is available to users on both free and paid tiers. Edison Analysis is a modified version of the data analysis agent Kosmos uses in its trajectories. Try it out! One of the most important improvements over our previous data analysis agents has been the addition of a specialized data retrieval tool. Edison Analysis can either use this tool to access data, or can pull data down directly via API. To evaluate this tool, we ranked the most commonly used public data repositories across recent papers from BioRxiv, and created a new benchmark that measures the ability of a language agent system to retrieve raw data from those sources. Edison Analysis gets 71% on this benchmark, and we’ll be working to increase this over time. You can read more about our benchmarks in the our blog post, link below. Some features worth highlighting: 1. Edison Analysis produces a report on the analysis it runs, along with a Jupyter notebook that you can download to reproduce the analysis yourself. Every figure it produces is linked back to the specific lines of code used to produce the figure, to make it easy to reproduce. 2. It works well with both Python and R. 3. One of the best uses for Edison Analysis is to use it to retrieve datasets that you can then analyze with Kosmos. We have a bunch of major improvements to Edison Analysis coming in the next few months that we’re excited to share. In the meantime, congratulations to the team, especially Ludovico Mitchener, Jon Laurent, Conor Igoe , Alex Andonian, and many more.
1:12

Today, we’re pushing a major update to Edison Analysis, our data analysis agent, which is tuned for scientific research and SOTA across data analysis benchmarks. In contrast to Kosmos, which runs for 6-12 hours and produces tens of thousands of lines of code, Edison Analysis runs for seconds to minutes and is best for specific, well-defined computational tasks. It is available both on our platform under the Analysis tab, and via API, and costs only one credit per run, so it is available to users on both free and paid tiers. Edison Analysis is a modified version of the data analysis agent Kosmos uses in its trajectories. Try it out! One of the most important improvements over our previous data analysis agents has been the addition of a specialized data retrieval tool. Edison Analysis can either use this tool to access data, or can pull data down directly via API. To evaluate this tool, we ranked the most commonly used public data repositories across recent papers from BioRxiv, and created a new benchmark that measures the ability of a language agent system to retrieve raw data from those sources. Edison Analysis gets 71% on this benchmark, and we’ll be working to increase this over time. You can read more about our benchmarks in the our blog post, link below. Some features worth highlighting: 1. Edison Analysis produces a report on the analysis it runs, along with a Jupyter notebook that you can download to reproduce the analysis yourself. Every figure it produces is linked back to the specific lines of code used to produce the figure, to make it easy to reproduce. 2. It works well with both Python and R. 3. One of the best uses for Edison Analysis is to use it to retrieve datasets that you can then analyze with Kosmos. We have a bunch of major improvements to Edison Analysis coming in the next few months that we’re excited to share. In the meantime, congratulations to the team, especially Ludovico Mitchener, Jon Laurent, Conor Igoe , Alex Andonian, and many more.

Sam Rodriques

62,031 görüntüleme • 10 ay önce

Give Claude Code a semantic filesystem 🗃️🛠️ Giving Claude Code access to the right CLI tools over your filesystem turns it into a general agent capable of automating far more knowledge work beyond code - it can do dynamic financial/legal/medical/technical/backoffice analysis over any subset of documents. With our latest release of semtools 💫, you can now manually or *agentically* create a persistent workspace over any subset of files. This gives Claude Code the ability to get blazing-fast, local semantic search over any data, while still allowing it to chain with commands like grep/cat/etc. so that it can load in dynamic context instead of naive top-k vector search. The coding agent can dynamically index data and use those indexes, instead of having to rebuild it every time. So you get the benefits of fast search along with agentic reasoning over CLI tools mentioned above. Come check it out!
0:38

Give Claude Code a semantic filesystem 🗃️🛠️ Giving Claude Code access to the right CLI tools over your filesystem turns it into a general agent capable of automating far more knowledge work beyond code - it can do dynamic financial/legal/medical/technical/backoffice analysis over any subset of documents. With our latest release of semtools 💫, you can now manually or *agentically* create a persistent workspace over any subset of files. This gives Claude Code the ability to get blazing-fast, local semantic search over any data, while still allowing it to chain with commands like grep/cat/etc. so that it can load in dynamic context instead of naive top-k vector search. The coding agent can dynamically index data and use those indexes, instead of having to rebuild it every time. So you get the benefits of fast search along with agentic reasoning over CLI tools mentioned above. Come check it out!

Jerry Liu

77,373 görüntüleme • 1 yıl önce

Which LLM reasons best when it doesn't have all the information? Enter LLM Poker Arena to find out. It's a Poker Playing benchmark where top reasoning models play Texas Hold'em poker against each other. Claude Opus 4.5, GPT-5.2, Gemini 2.5 Pro, and Grok 4 all sit at the same table and play full tournaments to see who finishes with the chips. Poker is very different when it comes to reasoning. It has to balance probabilistic reasoning, opponent modeling and make decisions under uncertainty. Poker is an interesting evaluation because it tests reasoning under incomplete information, something most coding benchmarks do not capture. In this tournaments the rules are: - Each LLM starts with $1,000 chips - Small and big blinds start at $25 / $50 - Blinds double every 3 minutes - All models run in their reasoning or thinking modes After the first 5 tournaments: - Claude Opus 4.5 with Thinking has 3 wins - GPT-5.2 has 2 wins - Grok 4 and Gemini 2.5 Pro have 0 wins Early results suggest Claude performs quite well at poker as well. Also five is a very small sample size. Planning to run many more tournaments, publish the full benchmark data and add a prediction market on top of it. Thanks for the suggestion clipz. Much more coming as part of Poker Cities !! This was built on Replit ⠕ using their AI integrations, which made it straightforward to connect Claude, GPT, and Gemini. What model do you think wins after 100 tournaments?
0:22

Which LLM reasons best when it doesn't have all the information? Enter LLM Poker Arena to find out. It's a Poker Playing benchmark where top reasoning models play Texas Hold'em poker against each other. Claude Opus 4.5, GPT-5.2, Gemini 2.5 Pro, and Grok 4 all sit at the same table and play full tournaments to see who finishes with the chips. Poker is very different when it comes to reasoning. It has to balance probabilistic reasoning, opponent modeling and make decisions under uncertainty. Poker is an interesting evaluation because it tests reasoning under incomplete information, something most coding benchmarks do not capture. In this tournaments the rules are: - Each LLM starts with $1,000 chips - Small and big blinds start at $25 / $50 - Blinds double every 3 minutes - All models run in their reasoning or thinking modes After the first 5 tournaments: - Claude Opus 4.5 with Thinking has 3 wins - GPT-5.2 has 2 wins - Grok 4 and Gemini 2.5 Pro have 0 wins Early results suggest Claude performs quite well at poker as well. Also five is a very small sample size. Planning to run many more tournaments, publish the full benchmark data and add a prediction market on top of it. Thanks for the suggestion clipz. Much more coming as part of Poker Cities !! This was built on Replit ⠕ using their AI integrations, which made it straightforward to connect Claude, GPT, and Gemini. What model do you think wins after 100 tournaments?

Anshul Dhawan

32,192 görüntüleme • 8 ay önce

Qwopus on a single RTX 3090. Claude Opus 4.6 reasoning distilled into Qwen 3.5 27B dense, running through Claude's own coding agent (claude code). 29-35 tok/s with thinking mode on. the jinja bug that kills thinking on base Qwen doesn't carry over. harness and model matched. the base model would pause mid task on Claude Code. just stop generating. that's why i ran it through OpenCode, which handles stalled states automatically. this distilled version doesn't stall. it waits for tool outputs, reads them, selfcorrects when something breaks, and keeps going. i gave it a benchmark analysis task. went 9 minutes autonomous. wrote a README nobody asked for. zero steering. video is 5x speed but fully uncut. if you have a 3090, you can run this right now. free. no API. no subscription. opus structured reasoning on localhost. octopus invaders is next. same prompt that base qwen passed in 13 minutes and hermes 4.3 failed on 2x the hardware. i want to see if the distillation changes the outcome or just the style. more data soon.
1:53

Qwopus on a single RTX 3090. Claude Opus 4.6 reasoning distilled into Qwen 3.5 27B dense, running through Claude's own coding agent (claude code). 29-35 tok/s with thinking mode on. the jinja bug that kills thinking on base Qwen doesn't carry over. harness and model matched. the base model would pause mid task on Claude Code. just stop generating. that's why i ran it through OpenCode, which handles stalled states automatically. this distilled version doesn't stall. it waits for tool outputs, reads them, selfcorrects when something breaks, and keeps going. i gave it a benchmark analysis task. went 9 minutes autonomous. wrote a README nobody asked for. zero steering. video is 5x speed but fully uncut. if you have a 3090, you can run this right now. free. no API. no subscription. opus structured reasoning on localhost. octopus invaders is next. same prompt that base qwen passed in 13 minutes and hermes 4.3 failed on 2x the hardware. i want to see if the distillation changes the outcome or just the style. more data soon.

Sudo su

296,392 görüntüleme • 7 ay önce

“Real-world impact has come much faster with AI than it did with mobile.” -Mike Krieger AI is already changing budgets, workflows, and P&Ls. I sat with Mike Krieger, CPO at Anthropic. Here’s what he told me: • How AI is driving revenue today • Sonnet 4.5: Opus-level brains at 1/5 the cost • Agents that can run 30 hours on tough coding tasks • How Claude Code lands in the enterprise Watch our full conversation below 👇
58:29

“Real-world impact has come much faster with AI than it did with mobile.” -Mike Krieger AI is already changing budgets, workflows, and P&Ls. I sat with Mike Krieger, CPO at Anthropic. Here’s what he told me: • How AI is driving revenue today • Sonnet 4.5: Opus-level brains at 1/5 the cost • Agents that can run 30 hours on tough coding tasks • How Claude Code lands in the enterprise Watch our full conversation below 👇

Zain Kahn

739,802 görüntüleme • 1 yıl önce

this is pure f*cking treasure Claude Code tip: once Opus 5.5 is your main model, stop leaving Fable 5.1 sitting idle and stop burning Opus tokens on tasks Sonnet 5.5 can swarm put it on call with /goal claude --auto-mode "/goal all tests pass and lint is clean" Opus 5.5 keeps writing the code in auto mode Sonnet 5.5 swarms the routine work at medium Fable 5.1 inspects the full transcript and only speaks up at the turn boundary: → not yet met: am I drifting from acceptance criteria? → met: do the tests, diffs, and lint hold? → impossible: am I chasing an unreachable state? Fable grades. Opus ships JEV engineering is the same move one layer down: the forks that need no thinker (which file, which tool, retry or halt) go to JEV in under 16ms, and the big models only see the ones that split — the full tree > Opus 5.5 on high runs the main session > worker edits and patches code on Sonnet 5.5 > explorer indexes AST maps and call sites on Sonnet 5.5 > verifier runs tests and linters on Sonnet 5.5 > all three subagents on medium effort > Fable 5.1 on call as the Stop hook evaluator > JEV routes micro-forks under 16ms paste the tree and this prompt into Claude Code ↓ "Reconfigure my Claude Code setup around this tree: 1. Set the main session to Opus 5.5 on high effort with auto mode enabled. 2. Put Fable 5.1 on call as a session-scoped evaluator attached to the Stop hook: - Read-only transcript evaluation with zero tool calls - Three structured outcomes: not yet met (with steering notes), met (auto-clear), and impossible (abort early) 3. Spawn Sonnet 5.5 subagents on medium effort for worker, explorer, and verifier roles. Defer goal checks while background tasks run. 4. Route fast deterministic forks (tool resolution, path selection, auto-retries) through JEV. 5. Add one rule to CLAUDE.md: run long tasks with: claude --auto-mode \"/goal \" Show me every configuration change as a diff first. No edits until I say go." ↳
0:25

this is pure f*cking treasure Claude Code tip: once Opus 5.5 is your main model, stop leaving Fable 5.1 sitting idle and stop burning Opus tokens on tasks Sonnet 5.5 can swarm put it on call with /goal claude --auto-mode "/goal all tests pass and lint is clean" Opus 5.5 keeps writing the code in auto mode Sonnet 5.5 swarms the routine work at medium Fable 5.1 inspects the full transcript and only speaks up at the turn boundary: → not yet met: am I drifting from acceptance criteria? → met: do the tests, diffs, and lint hold? → impossible: am I chasing an unreachable state? Fable grades. Opus ships JEV engineering is the same move one layer down: the forks that need no thinker (which file, which tool, retry or halt) go to JEV in under 16ms, and the big models only see the ones that split — the full tree > Opus 5.5 on high runs the main session > worker edits and patches code on Sonnet 5.5 > explorer indexes AST maps and call sites on Sonnet 5.5 > verifier runs tests and linters on Sonnet 5.5 > all three subagents on medium effort > Fable 5.1 on call as the Stop hook evaluator > JEV routes micro-forks under 16ms paste the tree and this prompt into Claude Code ↓ "Reconfigure my Claude Code setup around this tree: 1. Set the main session to Opus 5.5 on high effort with auto mode enabled. 2. Put Fable 5.1 on call as a session-scoped evaluator attached to the Stop hook: - Read-only transcript evaluation with zero tool calls - Three structured outcomes: not yet met (with steering notes), met (auto-clear), and impossible (abort early) 3. Spawn Sonnet 5.5 subagents on medium effort for worker, explorer, and verifier roles. Defer goal checks while background tasks run. 4. Route fast deterministic forks (tool resolution, path selection, auto-retries) through JEV. 5. Add one rule to CLAUDE.md: run long tasks with: claude --auto-mode \"/goal \" Show me every configuration change as a diff first. No edits until I say go." ↳

mirku

41,414 görüntüleme • 5 gün önce

Anthropic's in trouble, again. The entire Claude experience is now available at 1/6th the price. Kimi now does everything Claude does, powered by K2.6, a 1-trillion-parameter MoE model that activates only 32B parameters per token. It covers all three features Claude has (Chat, Code, and Cowork): 1) Kimi Chat runs in four modes - Instant for fast responses - Thinking for deep reasoning - Agent for multi-step execution - and Agent Swarm for parallel workloads. There's a 262K context window across all of them. 2) Kimi Code is the open-source CLI coding agent with K2.6 as the default backend. K2.6 ranked #1 on OpenRouter's programming leaderboard by weekly usage. 3) Kimi Agent is the Cowork equivalent. It generates: - full websites with database and auth - presentation decks (editable PPTX output) - spreadsheets with formulas and charts - word docs and structured research reports. On top of this, Kimi K2.6 is also trained to decompose tasks into up to 300 parallel sub-agents. This helps it retain coherence even across 4,000+ tool calls in a single run, with sessions sustaining up to 13 hours. On SWE-Bench Pro: - Kimi K2.6 → 58.6 - GPT-5.4 xhigh → 57.7 - Gemini 3.1 Pro → 54.2 - Claude Opus 4.6 → 53.4 Kimi K2.6 model is open weights and self-hostable on 4x H100s in INT4. Find the link to the HuggingFace model page in the replies!
0:41

Anthropic's in trouble, again. The entire Claude experience is now available at 1/6th the price. Kimi now does everything Claude does, powered by K2.6, a 1-trillion-parameter MoE model that activates only 32B parameters per token. It covers all three features Claude has (Chat, Code, and Cowork): 1) Kimi Chat runs in four modes - Instant for fast responses - Thinking for deep reasoning - Agent for multi-step execution - and Agent Swarm for parallel workloads. There's a 262K context window across all of them. 2) Kimi Code is the open-source CLI coding agent with K2.6 as the default backend. K2.6 ranked #1 on OpenRouter's programming leaderboard by weekly usage. 3) Kimi Agent is the Cowork equivalent. It generates: - full websites with database and auth - presentation decks (editable PPTX output) - spreadsheets with formulas and charts - word docs and structured research reports. On top of this, Kimi K2.6 is also trained to decompose tasks into up to 300 parallel sub-agents. This helps it retain coherence even across 4,000+ tool calls in a single run, with sessions sustaining up to 13 hours. On SWE-Bench Pro: - Kimi K2.6 → 58.6 - GPT-5.4 xhigh → 57.7 - Gemini 3.1 Pro → 54.2 - Claude Opus 4.6 → 53.4 Kimi K2.6 model is open weights and self-hostable on 4x H100s in INT4. Find the link to the HuggingFace model page in the replies!

Avi Chawla

110,493 görüntüleme • 5 ay önce

PREDICTION MARKET RESEARCH JUST GOT KILLED BY ONE .MD FILE. The .md file in the video plugs any AI agent into 1,800 live data sources -> Polymarket orderbooks, satellite imagery, vessel tracking, NOAA weather, SEC filings, sports lines, and the top 100 KOL wallets. It's pref.trade. No APIs, no scraping, no signup and no card. An agent with this installed doesn't ask "What's the price". It pulls the orderbook depth on Polymarket, cross-references vessel positions in the Strait of Hormuz, scans the latest SEC filings on the names mentioned, and watches what the top 100 KOL wallets did in the last 4 hours. Before it makes a single call. The numbers are insane: > $0 in API fees. > $0 in data subscriptions. > 670+ capabilities behind a single endpoint. Every datapoint with full provenance back to the source. The mechanism is wild too: It's called Preference. An MCP server that gives any AI agent structured access to prediction markets -> Polymarket, Kalshi, Hyperliquid, dFlow AND the real-world signals that price them. Your agent asks one question, gets the full picture before it acts. It goes way past Polymarket: Smart-money mirroring on the top 100 wallets in real time. Cross-venue arb scanners and event-driven agents that watch tanker traffic in the Strait of Hormuz and trade oil-linked markets. Backtesting pipelines over historical data plus the world signals that moved each market. The model was never the bottleneck. The data was. One agent, one .md file and Live world data on tap. -> Retail still has 12 CoinGecko tabs open. Agents already have the orderbook. Full info and guide at Don't forget to save.
0:34

PREDICTION MARKET RESEARCH JUST GOT KILLED BY ONE .MD FILE. The .md file in the video plugs any AI agent into 1,800 live data sources -> Polymarket orderbooks, satellite imagery, vessel tracking, NOAA weather, SEC filings, sports lines, and the top 100 KOL wallets. It's pref.trade. No APIs, no scraping, no signup and no card. An agent with this installed doesn't ask "What's the price". It pulls the orderbook depth on Polymarket, cross-references vessel positions in the Strait of Hormuz, scans the latest SEC filings on the names mentioned, and watches what the top 100 KOL wallets did in the last 4 hours. Before it makes a single call. The numbers are insane: > $0 in API fees. > $0 in data subscriptions. > 670+ capabilities behind a single endpoint. Every datapoint with full provenance back to the source. The mechanism is wild too: It's called Preference. An MCP server that gives any AI agent structured access to prediction markets -> Polymarket, Kalshi, Hyperliquid, dFlow AND the real-world signals that price them. Your agent asks one question, gets the full picture before it acts. It goes way past Polymarket: Smart-money mirroring on the top 100 wallets in real time. Cross-venue arb scanners and event-driven agents that watch tanker traffic in the Strait of Hormuz and trade oil-linked markets. Backtesting pipelines over historical data plus the world signals that moved each market. The model was never the bottleneck. The data was. One agent, one .md file and Live world data on tap. -> Retail still has 12 CoinGecko tabs open. Agents already have the orderbook. Full info and guide at Don't forget to save.

slash1s

61,850 görüntüleme • 4 ay önce

MiniMax M2.5 > Claude Opus 4.6 I have been using it for a couple of days and have never hit the limit. I am currently on the Coding Plus plan which gives me 300 prompts per 5 hours. My new setup is OpenCode + MiniMax M2.5 What I have noticed: M2.5 – handles multi-step coding workflows and repo-level reasoning more reliably – better at chaining actions. think: generate test, fix bug, refactor, repeat – high TPS throughput M2.5 hits ~80%+ on SWE-Bench coding benchmarks on par with Opus and runs these tasks 37% faster than its predecessor while costing ~1/10th - 1/20th the price. Check out the video.
3:49

MiniMax M2.5 > Claude Opus 4.6 I have been using it for a couple of days and have never hit the limit. I am currently on the Coding Plus plan which gives me 300 prompts per 5 hours. My new setup is OpenCode + MiniMax M2.5 What I have noticed: M2.5 – handles multi-step coding workflows and repo-level reasoning more reliably – better at chaining actions. think: generate test, fix bug, refactor, repeat – high TPS throughput M2.5 hits ~80%+ on SWE-Bench coding benchmarks on par with Opus and runs these tasks 37% faster than its predecessor while costing ~1/10th - 1/20th the price. Check out the video.

Pratham

184,843 görüntüleme • 7 ay önce

ANTHROPIC LEAKED A FILE WHERE 5 AGENTS WITH JEV BUILD A $1.2M A YEAR SYSTEM IN 30 MINUTES you pay $3.70 and walk away with an asset that can do $1.2M a year repo → data model → API → UI → evals no layer starts until the one under it is closed, and that edge is what turns five scripts into a system the operator on Haiku 4.5 brings up the repo and CI, the architect on Opus 5 lays down the data model and migrations two builders on Sonnet 5 run API and UI in parallel, and the reviewer on Opus 5.5 can keep finished code out of prod the evals were sitting in the file before the first line of code existed at minute zero there was not a single file, by the end it is a working product with payments and tests five agents cover in half an hour what a human team bills for a quarter JEV hands one line between layers instead of the whole history, so the next agent never re-reads the project and the run costs $3.70 instead of $370 the build takes 30 minutes, the first customers show up weeks later save this and paste it into Claude Code - let it assemble those 5 layers for you ↓
1:20

ANTHROPIC LEAKED A FILE WHERE 5 AGENTS WITH JEV BUILD A $1.2M A YEAR SYSTEM IN 30 MINUTES you pay $3.70 and walk away with an asset that can do $1.2M a year repo → data model → API → UI → evals no layer starts until the one under it is closed, and that edge is what turns five scripts into a system the operator on Haiku 4.5 brings up the repo and CI, the architect on Opus 5 lays down the data model and migrations two builders on Sonnet 5 run API and UI in parallel, and the reviewer on Opus 5.5 can keep finished code out of prod the evals were sitting in the file before the first line of code existed at minute zero there was not a single file, by the end it is a working product with payments and tests five agents cover in half an hour what a human team bills for a quarter JEV hands one line between layers instead of the whole history, so the next agent never re-reads the project and the run costs $3.70 instead of $370 the build takes 30 minutes, the first customers show up weeks later save this and paste it into Claude Code - let it assemble those 5 layers for you ↓

Sprytix

14,208 görüntüleme • 16 gün önce

A Bloomberg Terminal costs $30,000/yr and still can't do a fraction of what Perplexity Computer just launched today 💻 It now connects directly to your bank accounts, credit cards, loans, and brokerage accounts through Plaid. Your full financial picture, from monthly spending to net worth to individual stock positions, sitting on top of 40+ live finance data sources including SEC filings, FactSet, S&P Global, and Coinbase. Every dollar you earn, spend, owe, and invest, cross-referenced against institutional-grade data in real time. You can walk up to this thing and say: → Run a risk analysis on my portfolio against the current tariff environment → Show me where my spending spiked last month and what's driving it → Build a net worth dashboard that tracks everything in one place → Flag any holdings that overlap with what insiders have been selling this quarter And it just does it. Pulls from your linked accounts, cross-references SEC filings, builds the output, and delivers a finished product. The system running underneath is Perplexity Computer. It orchestrates 19 models simultaneously, breaks any goal into subtasks, spins up specialized agents for each one, and keeps working after you walk away. One model handles the reasoning. Another does the research. Another writes the code. Another builds the visualization. All coordinated automatically. Last month they launched with brokerage data only and someone built a Bloomberg Terminal clone in a single afternoon. That post did 7.5 million views. Now they've expanded to your entire financial life: checking, savings, credit cards, loans, and investments all in one place. Wall Street pays $30K a year for a terminal with 30,000 function commands built over four decades. It won't replace Bloomberg for institutional traders executing billion-dollar orders. But for everyone else, the gap just got a lot smaller.
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A Bloomberg Terminal costs $30,000/yr and still can't do a fraction of what Perplexity Computer just launched today 💻 It now connects directly to your bank accounts, credit cards, loans, and brokerage accounts through Plaid. Your full financial picture, from monthly spending to net worth to individual stock positions, sitting on top of 40+ live finance data sources including SEC filings, FactSet, S&P Global, and Coinbase. Every dollar you earn, spend, owe, and invest, cross-referenced against institutional-grade data in real time. You can walk up to this thing and say: → Run a risk analysis on my portfolio against the current tariff environment → Show me where my spending spiked last month and what's driving it → Build a net worth dashboard that tracks everything in one place → Flag any holdings that overlap with what insiders have been selling this quarter And it just does it. Pulls from your linked accounts, cross-references SEC filings, builds the output, and delivers a finished product. The system running underneath is Perplexity Computer. It orchestrates 19 models simultaneously, breaks any goal into subtasks, spins up specialized agents for each one, and keeps working after you walk away. One model handles the reasoning. Another does the research. Another writes the code. Another builds the visualization. All coordinated automatically. Last month they launched with brokerage data only and someone built a Bloomberg Terminal clone in a single afternoon. That post did 7.5 million views. Now they've expanded to your entire financial life: checking, savings, credit cards, loans, and investments all in one place. Wall Street pays $30K a year for a terminal with 30,000 function commands built over four decades. It won't replace Bloomberg for institutional traders executing billion-dollar orders. But for everyone else, the gap just got a lot smaller.

Josh Kale

151,395 görüntüleme • 6 ay önce

This workflow will save you thousands with Claude. Run Opus 5.5, Sonnet 5.5, Haiku 5.5, and Fable 5.1 together, and stop spending Opus tokens on work that doesn't need Opus. The entire idea in one line: Opus plans, Sonnet edits, Haiku reads, Fable reviews at decision points. Roles, broken down: - Opus 5.5, high effort, owns the plan and reviews the final code - Sonnet 5.5, medium effort, is the worker: edits files, runs tests - Haiku 5.5, low effort, splits into explorer (searches and reads the codebase) and researcher (pulls docs). it's the first Haiku with an effort setting - Fable 5.1, set with /advisor fable. Opus calls it at decision points, and it gets the full transcript each time Three moments Opus tends to call it: → before committing to a plan: is this the right approach? → the same error shows up again: is this going nowhere? → before marking the task done: did something get skipped? Why Haiku only reads: Anthropic's launch post says Sonnet 5.5 and Opus 5.5 are still the better choice for complex agentic coding (Terminal-Bench 4.0: 39.2% for Haiku 5.5, 70.6% for Sonnet 5.5) and that Haiku 5.5 fits narrowly scoped subagent work. so it gets the lookups, not the edits. Anyone still running one model for everything is paying $4 per million input tokens to check whether a file exists. Haiku 5.5 does it for $0.10. Drop this into Claude Code 👇 "Rebuild my Claude Code setup around this structure: Confirm Claude Code is v2.1.293 or later, so the haiku alias resolves to Haiku 5.5. If it isn't, stop and tell me to run claude update. Look through ~/.claude/agents and .claude/agents for subagents already covering explorer, worker, and researcher. Only create new ones for roles that are missing. Set model: haiku, effort: low on explorer and researcher, with no Edit or Write tools. Set model: sonnet, effort: medium on worker. If an existing subagent is locked to a different model, leave it as is and just list it. Name the explorer subagent Explore so it overrides the built-in one, which otherwise runs on my main model. In ~/.claude/settings.json, set advisorModel to fable, and set effortLevel to high for claude-opus-5-5 under modelSettings. A top-level effortLevel in user settings doesn't apply to Opus 5.5. Check for anything disabling the advisor: CLAUDE_CODE_DISABLE_ADVISOR_TOOL, DISABLE_TELEMETRY, or anything blocking feature-flag fetches. Also check CLAUDE_CODE_EFFORT_LEVEL, which overrides subagent effort settings, and CLAUDE_CODE_SUBAGENT_MODEL_FORCE, which makes Claude Code ignore subagent model fields. Report what you find. Don't change any of it yet. Add one line to ~/.claude/CLAUDE.md: consult the advisor before a big plan, when the same error shows up twice, and before marking a long task done. Show every change as a diff first. Wait for my go-ahead before touching anything."
0:21

This workflow will save you thousands with Claude. Run Opus 5.5, Sonnet 5.5, Haiku 5.5, and Fable 5.1 together, and stop spending Opus tokens on work that doesn't need Opus. The entire idea in one line: Opus plans, Sonnet edits, Haiku reads, Fable reviews at decision points. Roles, broken down: - Opus 5.5, high effort, owns the plan and reviews the final code - Sonnet 5.5, medium effort, is the worker: edits files, runs tests - Haiku 5.5, low effort, splits into explorer (searches and reads the codebase) and researcher (pulls docs). it's the first Haiku with an effort setting - Fable 5.1, set with /advisor fable. Opus calls it at decision points, and it gets the full transcript each time Three moments Opus tends to call it: → before committing to a plan: is this the right approach? → the same error shows up again: is this going nowhere? → before marking the task done: did something get skipped? Why Haiku only reads: Anthropic's launch post says Sonnet 5.5 and Opus 5.5 are still the better choice for complex agentic coding (Terminal-Bench 4.0: 39.2% for Haiku 5.5, 70.6% for Sonnet 5.5) and that Haiku 5.5 fits narrowly scoped subagent work. so it gets the lookups, not the edits. Anyone still running one model for everything is paying $4 per million input tokens to check whether a file exists. Haiku 5.5 does it for $0.10. Drop this into Claude Code 👇 "Rebuild my Claude Code setup around this structure: Confirm Claude Code is v2.1.293 or later, so the haiku alias resolves to Haiku 5.5. If it isn't, stop and tell me to run claude update. Look through ~/.claude/agents and .claude/agents for subagents already covering explorer, worker, and researcher. Only create new ones for roles that are missing. Set model: haiku, effort: low on explorer and researcher, with no Edit or Write tools. Set model: sonnet, effort: medium on worker. If an existing subagent is locked to a different model, leave it as is and just list it. Name the explorer subagent Explore so it overrides the built-in one, which otherwise runs on my main model. In ~/.claude/settings.json, set advisorModel to fable, and set effortLevel to high for claude-opus-5-5 under modelSettings. A top-level effortLevel in user settings doesn't apply to Opus 5.5. Check for anything disabling the advisor: CLAUDE_CODE_DISABLE_ADVISOR_TOOL, DISABLE_TELEMETRY, or anything blocking feature-flag fetches. Also check CLAUDE_CODE_EFFORT_LEVEL, which overrides subagent effort settings, and CLAUDE_CODE_SUBAGENT_MODEL_FORCE, which makes Claude Code ignore subagent model fields. Report what you find. Don't change any of it yet. Add one line to ~/.claude/CLAUDE.md: consult the advisor before a big plan, when the same error shows up twice, and before marking a long task done. Show every change as a diff first. Wait for my go-ahead before touching anything."

Alvaro Cintas

55,249 görüntüleme • 4 gün önce

I used Claude Code Skills + souped up PDF parsing to create an M&A deal comp agent 🤖 Given a directory of public M&A filings (DEF 14A), it parses and analyzes each pdf, and generates an Excel sheet with deal terms and comparables. 1️⃣ The native parsing is pypdf which sucks, so I swapped it out with LlamaIndex semtools. This uses LlamaCloud for parsing - letting it handle deeply complex financial tables, charts, and anything else you throw at it. 2️⃣ Claude has native skills to write Excel spreadsheets! Full prompt is in the video below 👇 LlamaCloud + Semtools access also takes < 5 mins to setup, come check it out! Some notes: - disclaimer: Some of the values in the Excel spreadsheet are 100x bigger because they’re formatted as percentages not raw values - We’re working on native Claude skill integrations too! Semtools: LlamaCloud:
1:09

I used Claude Code Skills + souped up PDF parsing to create an M&A deal comp agent 🤖 Given a directory of public M&A filings (DEF 14A), it parses and analyzes each pdf, and generates an Excel sheet with deal terms and comparables. 1️⃣ The native parsing is pypdf which sucks, so I swapped it out with LlamaIndex semtools. This uses LlamaCloud for parsing - letting it handle deeply complex financial tables, charts, and anything else you throw at it. 2️⃣ Claude has native skills to write Excel spreadsheets! Full prompt is in the video below 👇 LlamaCloud + Semtools access also takes < 5 mins to setup, come check it out! Some notes: - disclaimer: Some of the values in the Excel spreadsheet are 100x bigger because they’re formatted as percentages not raw values - We’re working on native Claude skill integrations too! Semtools: LlamaCloud:

Jerry Liu

46,748 görüntüleme • 11 ay önce

Someone ran Claude Code on an e-ink notebook and the slowest screen in the world suddenly turned out to be the best home for an AI that already thinks one word at a time. This is the reMarkable Paper Pro, a paper tablet for notes with no browser and no social media and not a single app. He went into it over SSH and brought up Claude Code on Opus 4.8 on Claude Max and typed right into the terminal on the paper screen: "hello reddit, this is ssh terminal on rmpp". For years this screen got slammed for one thing. E-ink is too slow and it draws with a delay and it ghosts and it is no good for real work. But Claude itself puts out a thought one word at a time. And here is what came out of it: the very thing that killed the paper screen for normal software lined up perfectly with the pace of the AI. There is no more lag because there is nothing left to lag. And then come the things no monitor can give you. Your eyes do not get tired. You can watch Opus think on max effort for an hour and it feels like reading a book and not staring into a backlight. Nothing distracts you. Not a single notification and not a single tab and just a cursor and an agent that writes code while you simply watch the page. The charge lasts for days. E-ink barely touches the battery so Claude can grind on a task all night long and the tablet is still alive by morning. And it weighs as much as a notebook. The whole work setup now fits into a bag like a notepad with a stylus on top. Everything on the screen is for real: Claude Code v2.1.162 and bypass permissions on and Opus going off to think on max effort right on the e-ink. In my opinion this is the most unexpected home for an AI this year. Not a farm of graphics cards and not a wall of monitors but a quiet sheet of paper on a coffee table where the most powerful Claude writes code one word at a time like a pen.
0:52

Someone ran Claude Code on an e-ink notebook and the slowest screen in the world suddenly turned out to be the best home for an AI that already thinks one word at a time. This is the reMarkable Paper Pro, a paper tablet for notes with no browser and no social media and not a single app. He went into it over SSH and brought up Claude Code on Opus 4.8 on Claude Max and typed right into the terminal on the paper screen: "hello reddit, this is ssh terminal on rmpp". For years this screen got slammed for one thing. E-ink is too slow and it draws with a delay and it ghosts and it is no good for real work. But Claude itself puts out a thought one word at a time. And here is what came out of it: the very thing that killed the paper screen for normal software lined up perfectly with the pace of the AI. There is no more lag because there is nothing left to lag. And then come the things no monitor can give you. Your eyes do not get tired. You can watch Opus think on max effort for an hour and it feels like reading a book and not staring into a backlight. Nothing distracts you. Not a single notification and not a single tab and just a cursor and an agent that writes code while you simply watch the page. The charge lasts for days. E-ink barely touches the battery so Claude can grind on a task all night long and the tablet is still alive by morning. And it weighs as much as a notebook. The whole work setup now fits into a bag like a notepad with a stylus on top. Everything on the screen is for real: Claude Code v2.1.162 and bypass permissions on and Opus going off to think on max effort right on the e-ink. In my opinion this is the most unexpected home for an AI this year. Not a farm of graphics cards and not a wall of monitors but a quiet sheet of paper on a coffee table where the most powerful Claude writes code one word at a time like a pen.

Blaze

424,610 görüntüleme • 3 ay önce

Introducing Yellowcake Analytics v2. Yellowcake Analytics is a free research platform I’ve been building for the uranium sector. It started as a simple tracker for resource estimates pulled from SEC filings. These updates make it a full research stack and one of the most comprehensive free resources for uranium investing. Market and sector data * AI datacenter deal tracker: hyperscaler nuclear deals, converted into pounds of uranium per year * Interactive map of 400+ mines, reactors, and fuel-cycle facilities worldwide * Physical uranium tracker: SPUT and peers, ~110 Mlb across funds * US and EU import monitor: Russian uranium imports tracked against the waiver caps that expire in 2028 Company research * Stock pages for 17 miners: financials from filings, real-time pricing, and resource data in one place * AI-generated summaries of new SEC filings and uranium news, updated daily * Guidance tracker: what producers promised vs what they delivered Analyst tools * Customizable watchlist with email alerts: spot moves, SPUT premium shifts, and new filings from tickers you follow * Screener to compare miners on resource size, grade, market cap, and cash position Every number links to its primary source, so you can verify yourself.
0:29

Introducing Yellowcake Analytics v2. Yellowcake Analytics is a free research platform I’ve been building for the uranium sector. It started as a simple tracker for resource estimates pulled from SEC filings. These updates make it a full research stack and one of the most comprehensive free resources for uranium investing. Market and sector data * AI datacenter deal tracker: hyperscaler nuclear deals, converted into pounds of uranium per year * Interactive map of 400+ mines, reactors, and fuel-cycle facilities worldwide * Physical uranium tracker: SPUT and peers, ~110 Mlb across funds * US and EU import monitor: Russian uranium imports tracked against the waiver caps that expire in 2028 Company research * Stock pages for 17 miners: financials from filings, real-time pricing, and resource data in one place * AI-generated summaries of new SEC filings and uranium news, updated daily * Guidance tracker: what producers promised vs what they delivered Analyst tools * Customizable watchlist with email alerts: spot moves, SPUT premium shifts, and new filings from tickers you follow * Screener to compare miners on resource size, grade, market cap, and cash position Every number links to its primary source, so you can verify yourself.

Patrick Scott

18,648 görüntüleme • 2 ay önce