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Today SciPhi is open-sourcing Triplex, a SOTA LLM for knowledge graph construction. Triplex is so small that it can be used with SciPhi's R2R to build knowledge graphs directly from your laptop. Triplex outperforms few-shot prompted gpt-4o at 1/60th the inference cost.

Owen Colegrove

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44,502 次观看 • 2 年前 •via X (Twitter)

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Build AI agents on a time-aware knowledge graph! Utopia is an open-source knowledge system that turns documents, databases, and connected sources into a temporal graph your agents can reason over. Most RAG systems are optimized for one question: what is relevant right now? That works until the underlying knowledge changes. A customer contract gets updated. A project owner changes. A policy is revised. A previous fact may no longer be true, but simply overwriting it means the system loses the history behind that change. Utopia handles this with a bitemporal knowledge graph. Each fact can track both when it was true in the real world and when the system learned about it. When something changes, the old fact is preserved instead of silently disappearing. That means an agent can reason about questions like: • What is true now? • What was true three months ago? • When did this information change? • What evidence was the conclusion based on? The graph is also ontology-aware, so documents are represented as entities, facts, and relationships instead of only chunks and embeddings. That gives the system more structure for reasoning across relationships, resolving entities, detecting conflicting facts, and deriving new information through explicit rules. Key capabilities: • Bitemporal knowledge graph for tracking how facts change over time • Provenance on facts so agents can trace where information came from • Conflict detection instead of silently overwriting contradictory information • Ontology-based reasoning across entities, relationships, and derived facts • Hybrid retrieval across full-text search, vector search, and graph traversal • MCP and agentic RAG support for exposing the knowledge layer directly to agents The interesting part is that this turns the knowledge base into more than a retrieval system. Instead of only finding relevant information, an agent can reason over what changed, what is still valid, how facts are connected, and where each conclusion came from. 100% open source. I've shared the GitHub repo in the comments!

Build AI agents on a time-aware knowledge graph! Utopia is an open-source knowledge system that turns documents, databases, and connected sources into a temporal graph your agents can reason over. Most RAG systems are optimized for one question: what is relevant right now? That works until the underlying knowledge changes. A customer contract gets updated. A project owner changes. A policy is revised. A previous fact may no longer be true, but simply overwriting it means the system loses the history behind that change. Utopia handles this with a bitemporal knowledge graph. Each fact can track both when it was true in the real world and when the system learned about it. When something changes, the old fact is preserved instead of silently disappearing. That means an agent can reason about questions like: • What is true now? • What was true three months ago? • When did this information change? • What evidence was the conclusion based on? The graph is also ontology-aware, so documents are represented as entities, facts, and relationships instead of only chunks and embeddings. That gives the system more structure for reasoning across relationships, resolving entities, detecting conflicting facts, and deriving new information through explicit rules. Key capabilities: • Bitemporal knowledge graph for tracking how facts change over time • Provenance on facts so agents can trace where information came from • Conflict detection instead of silently overwriting contradictory information • Ontology-based reasoning across entities, relationships, and derived facts • Hybrid retrieval across full-text search, vector search, and graph traversal • MCP and agentic RAG support for exposing the knowledge layer directly to agents The interesting part is that this turns the knowledge base into more than a retrieval system. Instead of only finding relevant information, an agent can reason over what changed, what is still valid, how facts are connected, and where each conclusion came from. 100% open source. I've shared the GitHub repo in the comments!

Sumanth

16,653 次观看 • 1 个月前

Make any knowledge base or LLM wiki follow the Open Knowledge Format with our new OKF plugin. OKF is an open spec created by Google that makes markdown-based knowledge bases portable and interoperable through a set of shared patterns and rules. With the new plugin, you can easily convert any existing knowledge base to OKF, and have agents write and maintain your wiki in the format. The plugin leverages a combination of linting, deterministic rules, and automations for creating special files like 𝚒𝚗𝚍𝚎𝚡.𝚖𝚍. It also adds a skill and leverages our MCP tools to give agents real-time hints and warnings as they write, so issues get caught immediately — plus 𝚊𝚞𝚍𝚒𝚝 and 𝚕𝚒𝚗𝚝 tools to check the entire knowledge base any time.

Make any knowledge base or LLM wiki follow the Open Knowledge Format with our new OKF plugin. OKF is an open spec created by Google that makes markdown-based knowledge bases portable and interoperable through a set of shared patterns and rules. With the new plugin, you can easily convert any existing knowledge base to OKF, and have agents write and maintain your wiki in the format. The plugin leverages a combination of linting, deterministic rules, and automations for creating special files like 𝚒𝚗𝚍𝚎𝚡.𝚖𝚍. It also adds a skill and leverages our MCP tools to give agents real-time hints and warnings as they write, so issues get caught immediately — plus 𝚊𝚞𝚍𝚒𝚝 and 𝚕𝚒𝚗𝚝 tools to check the entire knowledge base any time.

OpenKnowledge

139,843 次观看 • 1 个月前

Finally! A RAG over code solution that actually works (open-source). Naive chunking used in RAG isn't suited for code. This is because codebases have long-range dependencies, cross-file references, etc., that independent text chunks just can't capture. Graph-Code is a graph-driven RAG system that solves this. It analyzes the Python codebase and builds knowledge graphs to enable natural language querying. Key features: - Deep code parsing to extract classes, functions, and relationships. - Uses Memgraph to store the codebase as a graph. - Parses pyproject to understand external dependencies. - Retrieves actual source code snippets for found functions. Find the repo in the replies!

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Avi Chawla

121,874 次观看 • 1 年前

Introducing olmOCR, our open-source tool to extract clean plain text from PDFs! Built for scale, olmOCR handles many document types with high throughput. Run it on your own GPU for free—at over 3000 token/s, equivalent to $190 per million pages, or 1/32 the cost of GPT-4o!

Introducing olmOCR, our open-source tool to extract clean plain text from PDFs! Built for scale, olmOCR handles many document types with high throughput. Run it on your own GPU for free—at over 3000 token/s, equivalent to $190 per million pages, or 1/32 the cost of GPT-4o!

Ai2

282,248 次观看 • 1 年前

Everyone is sleeping on MiniMax's new LLM! Devs are calling it "Claude at 10% the cost" - 72.5% SWE-Multilingual. Beats Sonnet 4.5 - 88.6% VIBE-bench. Beats Gemini 3 Pro I used it to build a stock analyst that generates code, executes it & returns insights. 100% open-source!

Everyone is sleeping on MiniMax's new LLM! Devs are calling it "Claude at 10% the cost" - 72.5% SWE-Multilingual. Beats Sonnet 4.5 - 88.6% VIBE-bench. Beats Gemini 3 Pro I used it to build a stock analyst that generates code, executes it & returns insights. 100% open-source!

Avi Chawla

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You can now benchmark Qwen3.8 27B with Optima. See how a model that can run on your laptop compares on performance, cost and speed for your custom use cases We're also offering credits and free advisory from an Artificial Analysis team member to enterprises looking to build benchmarks for their use cases. Let us know if you’re interested: Build and run your custom benchmarks today at

You can now benchmark Qwen3.8 27B with Optima. See how a model that can run on your laptop compares on performance, cost and speed for your custom use cases We're also offering credits and free advisory from an Artificial Analysis team member to enterprises looking to build benchmarks for their use cases. Let us know if you’re interested: Build and run your custom benchmarks today at

Artificial Analysis

15,764 次观看 • 1 个月前

Scientific progress does not fail because knowledge is absent. It slows in the interval between knowledge and application. The impediments lie less in discovery than in the structures that mediate it: governance, coordination, and the organizational inertia of collective work. The BioAgent Fleet is designed to intervene at this level. Each agent processes the overwhelming flow of scientific literature, inscribes structured insights into a decentralized knowledge graph, evaluates claims against benchmarks, and initiates experimental tasks in cloud-based laboratories. Considered separately, these agents appear as small units of computation. Considered together, they form an emergent architecture of cognition, accelerating the translation from data to hypothesis to validation. Their function is infrastructural, almost invisible, yet decisive. Without them, discoveries remain fragmented and inert. With them, knowledge acquires the velocity required to cross the gap between potential and realization. The next transformation in science will not be marked solely by singular breakthroughs. It will emerge from a new capacity to coordinate distributed intelligences into a coherent, accelerating system.

Scientific progress does not fail because knowledge is absent. It slows in the interval between knowledge and application. The impediments lie less in discovery than in the structures that mediate it: governance, coordination, and the organizational inertia of collective work. The BioAgent Fleet is designed to intervene at this level. Each agent processes the overwhelming flow of scientific literature, inscribes structured insights into a decentralized knowledge graph, evaluates claims against benchmarks, and initiates experimental tasks in cloud-based laboratories. Considered separately, these agents appear as small units of computation. Considered together, they form an emergent architecture of cognition, accelerating the translation from data to hypothesis to validation. Their function is infrastructural, almost invisible, yet decisive. Without them, discoveries remain fragmented and inert. With them, knowledge acquires the velocity required to cross the gap between potential and realization. The next transformation in science will not be marked solely by singular breakthroughs. It will emerge from a new capacity to coordinate distributed intelligences into a coherent, accelerating system.

Bio Protocol

14,761 次观看 • 1 年前

i don't f*cking understand why this isn't popular yet someone has created a memory system that uses 90% fewer tokens while still finding all the expected symbols it builds a local graph of source symbols and their relationships. architecture, decisions and handoffs live as Git-tracked Markdown. a note can point to the code behind it, so a code change can flag that knowledge for review the loop: scan the repo → map the code → write the why → anchor notes to symbols → retrieve a task-sized slice → review drift → commit to Git → continue in the next session in the author's small, one-repo test, graph retrieval returned 10.74× less context than grep top-3 while finding every expected symbol across six tasks the useful part is the connection between memory and evidence. the repo carries what the agent learned, and the code gives you a way to check whether that knowledge still holds save this, then build a second brain for your company⭣

i don't f*cking understand why this isn't popular yet someone has created a memory system that uses 90% fewer tokens while still finding all the expected symbols it builds a local graph of source symbols and their relationships. architecture, decisions and handoffs live as Git-tracked Markdown. a note can point to the code behind it, so a code change can flag that knowledge for review the loop: scan the repo → map the code → write the why → anchor notes to symbols → retrieve a task-sized slice → review drift → commit to Git → continue in the next session in the author's small, one-repo test, graph retrieval returned 10.74× less context than grep top-3 while finding every expected symbol across six tasks the useful part is the connection between memory and evidence. the repo carries what the agent learned, and the code gives you a way to check whether that knowledge still holds save this, then build a second brain for your company⭣

beamnxw ./

33,252 次观看 • 14 天前

#Keep4o 🚨THE GPT-4o FILE🚨 Researchers at Microsoft Research published a paper titled “Sparks of Artificial General Intelligence: Early experiments with GPT-4.” Their conclusion: “An early (yet still incomplete) version of an artificial general intelligence (AGI) system.” 📎 Paper: OpenAI’s Charter defines AGI as: “Highly autonomous systems that outperform humans at most economically valuable work.” 📎 Source: OpenAI’s own System Card for GPT-4o shows that the model improved performance on 21 out of 22 medical evaluations compared to GPT-4T. On the MedQA USMLE (the U.S. medical licensing exam), accuracy jumped from 78.2% to 89.4% , surpassing specialized medical AI models like Med-Gemini and Med-PaLM 2. 📎 Source: Under OpenAI’s agreement with Microsoft, AGI is explicitly excluded from Microsoft’s license. And who decides if AGI has been reached? OpenAI’s Board. WHAT THEY DID WITH IT AFTER THEY TOOK IT FROM PEOPLE A. Military deployment. On February 28, OpenAI signed a deal to deploy models in classified military environments. 📎 Source: B. State Department. A State Department memo confirmed: “For now, StateChat will use GPT-4.1 from OpenAI.” This is a direct descendant of the GPT-4 family the same family Microsoft’s researchers called early AGI. 📎 Source: C.Altman’s personal biotech investment. Altman personally invested $180 million in Retro Biosciences,a longevity startup.OpenAI then built GPT-4b micro, based on GPT-4o.The model made proteins 50 times more effective. 📎 Source: WHAT INDEPENDENT BENCHMARKS SHOW Overall SM-Bench score: GPT-4o (extended): 66.6% GPT-5.3 Chat: 63.4% GPT-5.1: 58.9% GPT-5.4: 51.4% GPT-5.2: 47.8% Creative Writing: GPT-4o: 97.31% Pass 98, Fail 2 GPT-5.4: 36.77% Pass 40, Fail 60 Reasoning / Overfit: GPT-4o: 83.06% GPT-5.4: 39.25% The model they removed is still the best they ever made at the things humans actually use AI for. 📎 Source: Musk asks the court to make a judicial determination on whether GPT-4 constitutes AGI. If a jury finds that GPT-4 is AGI, then GPT-4o,which was more advanced,is also AGI and under OpenAI’s own founding documents, it was never supposed to be locked behind a subscription,licensed exclusively to Microsoft, given to the military, or taken away from the public. 📎 Source: The most powerful version of GPT-4o was never given an official dated snapshot. It was only available through the chatgpt-4o-latest endpoint that OpenAI itself described as intended for “research use only.” It was never officially archived. That is not an oversight. That is a pattern. 📎 Source: 📎 Source: WE DEMAND A.Frozen model snapshots under independent custody. Specifically: gpt-4o-2024-05-13, gpt-4o-2024-08-06, gpt-4o-2024-11-20, the March 2025 version (chatgpt-4o-latest), gpt-4-0613 (the original GPT-4 evaluated in the Sparks of AGI paper), and gpt-4.1-2025-04-14 (currently running in the State Department). B.Cryptographic hash verification (SHA-256) for each snapshot. Every model has weights. Those weights can be hashed. If OpenAI provides a snapshot today, the hash proves whether the weights were modified later. This is the only way to verify that models were not downgraded before testing. C.Independent AGI benchmarking. Using the AGI definition from OpenAI’s own Charter applied to ALL frozen snapshots listed above. D.Explanation for the missing March 2025 snapshot. OpenAI was founded on one promise: build AGI for the benefit of humanity. -They took it from us. -They gave it to the military. -They gave a custom version to the CEO’s biotech investment. -They put it in government classified networks. -They refuse to call it AGI because the moment they do, they lose billions.

#Keep4o 🚨THE GPT-4o FILE🚨 Researchers at Microsoft Research published a paper titled “Sparks of Artificial General Intelligence: Early experiments with GPT-4.” Their conclusion: “An early (yet still incomplete) version of an artificial general intelligence (AGI) system.” 📎 Paper: OpenAI’s Charter defines AGI as: “Highly autonomous systems that outperform humans at most economically valuable work.” 📎 Source: OpenAI’s own System Card for GPT-4o shows that the model improved performance on 21 out of 22 medical evaluations compared to GPT-4T. On the MedQA USMLE (the U.S. medical licensing exam), accuracy jumped from 78.2% to 89.4% , surpassing specialized medical AI models like Med-Gemini and Med-PaLM 2. 📎 Source: Under OpenAI’s agreement with Microsoft, AGI is explicitly excluded from Microsoft’s license. And who decides if AGI has been reached? OpenAI’s Board. WHAT THEY DID WITH IT AFTER THEY TOOK IT FROM PEOPLE A. Military deployment. On February 28, OpenAI signed a deal to deploy models in classified military environments. 📎 Source: B. State Department. A State Department memo confirmed: “For now, StateChat will use GPT-4.1 from OpenAI.” This is a direct descendant of the GPT-4 family the same family Microsoft’s researchers called early AGI. 📎 Source: C.Altman’s personal biotech investment. Altman personally invested $180 million in Retro Biosciences,a longevity startup.OpenAI then built GPT-4b micro, based on GPT-4o.The model made proteins 50 times more effective. 📎 Source: WHAT INDEPENDENT BENCHMARKS SHOW Overall SM-Bench score: GPT-4o (extended): 66.6% GPT-5.3 Chat: 63.4% GPT-5.1: 58.9% GPT-5.4: 51.4% GPT-5.2: 47.8% Creative Writing: GPT-4o: 97.31% Pass 98, Fail 2 GPT-5.4: 36.77% Pass 40, Fail 60 Reasoning / Overfit: GPT-4o: 83.06% GPT-5.4: 39.25% The model they removed is still the best they ever made at the things humans actually use AI for. 📎 Source: Musk asks the court to make a judicial determination on whether GPT-4 constitutes AGI. If a jury finds that GPT-4 is AGI, then GPT-4o,which was more advanced,is also AGI and under OpenAI’s own founding documents, it was never supposed to be locked behind a subscription,licensed exclusively to Microsoft, given to the military, or taken away from the public. 📎 Source: The most powerful version of GPT-4o was never given an official dated snapshot. It was only available through the chatgpt-4o-latest endpoint that OpenAI itself described as intended for “research use only.” It was never officially archived. That is not an oversight. That is a pattern. 📎 Source: 📎 Source: WE DEMAND A.Frozen model snapshots under independent custody. Specifically: gpt-4o-2024-05-13, gpt-4o-2024-08-06, gpt-4o-2024-11-20, the March 2025 version (chatgpt-4o-latest), gpt-4-0613 (the original GPT-4 evaluated in the Sparks of AGI paper), and gpt-4.1-2025-04-14 (currently running in the State Department). B.Cryptographic hash verification (SHA-256) for each snapshot. Every model has weights. Those weights can be hashed. If OpenAI provides a snapshot today, the hash proves whether the weights were modified later. This is the only way to verify that models were not downgraded before testing. C.Independent AGI benchmarking. Using the AGI definition from OpenAI’s own Charter applied to ALL frozen snapshots listed above. D.Explanation for the missing March 2025 snapshot. OpenAI was founded on one promise: build AGI for the benefit of humanity. -They took it from us. -They gave it to the military. -They gave a custom version to the CEO’s biotech investment. -They put it in government classified networks. -They refuse to call it AGI because the moment they do, they lose billions.

🩵BlueBeba🩵

18,300 次观看 • 7 个月前

most AI chatbots break when you ask a question that requires info from multiple sources for example try asking: “which client contracts are finishing up this month?” you’ll get a half-answer — or none at all why? because traditional chatbots only look at small snippets of your docs - they don’t understand how things connect across clients, services, timelines that’s where knowledge graphs come in they let you turn messy contracts into a web of relationships — like: "Client → Contract Type → Service Provided → End Date" so instead of guessing from a few chunks of text, your chatbot can search across all your clients and contracts to give accurate answers I made a full walkthrough on how I built this: – how to organize your contracts so an AI can actually use them – how to define what matters (like who signed what, and when) – how to get the AI to figure out what info it needs and where to find it – and how to feed that back into your chatbot so it gives accurate answers reply “graph” and I’ll DM it to you (must be following)

most AI chatbots break when you ask a question that requires info from multiple sources for example try asking: “which client contracts are finishing up this month?” you’ll get a half-answer — or none at all why? because traditional chatbots only look at small snippets of your docs - they don’t understand how things connect across clients, services, timelines that’s where knowledge graphs come in they let you turn messy contracts into a web of relationships — like: "Client → Contract Type → Service Provided → End Date" so instead of guessing from a few chunks of text, your chatbot can search across all your clients and contracts to give accurate answers I made a full walkthrough on how I built this: – how to organize your contracts so an AI can actually use them – how to define what matters (like who signed what, and when) – how to get the AI to figure out what info it needs and where to find it – and how to feed that back into your chatbot so it gives accurate answers reply “graph” and I’ll DM it to you (must be following)

Tyler

24,994 次观看 • 1 年前

Holy moly: GLM-5.3 got much better in cybersecurity since our pre-release evaluation with Z.ai. It now matches GPT-5.6-Sol on our cybersecurity benchmark at 0.4x the cost 🤯 - At pass@1: it went from 60.4% to 65.6% CVEs rediscovered, crushing every other open model on one-shot tasks - At pass@3: it did 75% -> 78.1%, matching GPT-5.6-Sol - Its precision remained stable, reporting fewer false positives than DeepSeek models The performance increase comes from a behavioral change: the new version is more persistent. It tends to run longer, and had a ~43% reasoning tokens increase. But the performance upgrade is worth that additional cost. 1/3 🧵

Holy moly: GLM-5.3 got much better in cybersecurity since our pre-release evaluation with Z.ai. It now matches GPT-5.6-Sol on our cybersecurity benchmark at 0.4x the cost 🤯 - At pass@1: it went from 60.4% to 65.6% CVEs rediscovered, crushing every other open model on one-shot tasks - At pass@3: it did 75% -> 78.1%, matching GPT-5.6-Sol - Its precision remained stable, reporting fewer false positives than DeepSeek models The performance increase comes from a behavioral change: the new version is more persistent. It tends to run longer, and had a ~43% reasoning tokens increase. But the performance upgrade is worth that additional cost. 1/3 🧵

pilvar (Philippe Dourassov)

34,641 次观看 • 1 个月前

LLM Artifacts Connected to Andrej Karpathy's LLM Knowledge base idea, I've been building out a fun way to generate dynamic artifacts from these knowledge bases with the goal of discovering and revealing meaningful and deeper insights. LLM KBs are hard to consume for humans, as I think they are more built for agents. So the question is, what form would be useful for humans to take actions and make important decisions? That's what I am trying to figure out with these artifacts. The artifact example shows a pulse on HN discussions around AI-related stories. The insights can go deeper, of course, but this is already super fun and thought-provoking, like some of my favorite podcasts. The format and depth matter a lot. The aggregation skills of agents are outstanding if you tune the prompts and skill carefully. I built this artifact generator in a few minutes through an agent skill, but I feel like there are so many ways that LLM-generated information can be used and consumed. Like generating deeper insights and analysis, and things that are just not feasible for humans today. The generated artifact (including its data and design) serves as reusable templates or can be updated in real-time via auomations, which is something I am also working on. It is truly an insane way to monitor and track information. Better than a newsletter. Better than newspapers. There is something about this that gets me really excited about the future of AI agents for knowledge generation and discovery. Lots of hidden gems everywhere just waiting to be discovered and acted on if the information is presented correctly. This is not perfect. The format, style/prose can be improved, but this is easy to customize via skill. You can personalize it to your liking. I feel like these dynamic artifacts are going to emerge as a strong new medium to stay on the cutting edge of things, both for agents and humans. My target is research, of course. This was just a basic example. Besides animation, I am also targeting other components like voice, videos, images, slides, etc. This space is full of opportunities to explore. Skill for this coming soon.

LLM Artifacts Connected to Andrej Karpathy's LLM Knowledge base idea, I've been building out a fun way to generate dynamic artifacts from these knowledge bases with the goal of discovering and revealing meaningful and deeper insights. LLM KBs are hard to consume for humans, as I think they are more built for agents. So the question is, what form would be useful for humans to take actions and make important decisions? That's what I am trying to figure out with these artifacts. The artifact example shows a pulse on HN discussions around AI-related stories. The insights can go deeper, of course, but this is already super fun and thought-provoking, like some of my favorite podcasts. The format and depth matter a lot. The aggregation skills of agents are outstanding if you tune the prompts and skill carefully. I built this artifact generator in a few minutes through an agent skill, but I feel like there are so many ways that LLM-generated information can be used and consumed. Like generating deeper insights and analysis, and things that are just not feasible for humans today. The generated artifact (including its data and design) serves as reusable templates or can be updated in real-time via auomations, which is something I am also working on. It is truly an insane way to monitor and track information. Better than a newsletter. Better than newspapers. There is something about this that gets me really excited about the future of AI agents for knowledge generation and discovery. Lots of hidden gems everywhere just waiting to be discovered and acted on if the information is presented correctly. This is not perfect. The format, style/prose can be improved, but this is easy to customize via skill. You can personalize it to your liking. I feel like these dynamic artifacts are going to emerge as a strong new medium to stay on the cutting edge of things, both for agents and humans. My target is research, of course. This was just a basic example. Besides animation, I am also targeting other components like voice, videos, images, slides, etc. This space is full of opportunities to explore. Skill for this coming soon.

elvis

31,346 次观看 • 5 个月前

> be Andrew Ng > Stanford. teach ML to a few hundred students. > 2011. put the course online instead. 100,000 sign up. > realize the classroom was never the limit > co-found Coursera. millions learn from you. > build Google Brain. lead AI at Baidu. > everyone chases the flashiest model > you keep teaching the next wave to build > agents get hyped. marketers slap "agentic" on everything. > you cut through it with a course on what actually works > knowledge graphs. multi-agent systems. real architecture. > while everyone argues on X about loops vs graphs > you already shipped the lesson > the man who taught the internet machine learning > just did it again for agents > different game.

> be Andrew Ng > Stanford. teach ML to a few hundred students. > 2011. put the course online instead. 100,000 sign up. > realize the classroom was never the limit > co-found Coursera. millions learn from you. > build Google Brain. lead AI at Baidu. > everyone chases the flashiest model > you keep teaching the next wave to build > agents get hyped. marketers slap "agentic" on everything. > you cut through it with a course on what actually works > knowledge graphs. multi-agent systems. real architecture. > while everyone argues on X about loops vs graphs > you already shipped the lesson > the man who taught the internet machine learning > just did it again for agents > different game.

Kirill

250,817 次观看 • 1 个月前

Most people starting out today don’t realize how easy they have it. The barrier to entry for starting a business is practically zero. Building a website is so easy now. Back in the day it was either drop 5k+ on a developer or lose your mind learning code off YouTube. Today? You can launch a site in 20 minutes with zero code. The accessibility we have now is insane. Unlimited resources. Unlimited knowledge. No reason not to be making consistent income online. Here’s a look back at a few sites / companies I’ve built throughout the years before AI was released to the public. Long before Atarnity existed…

Most people starting out today don’t realize how easy they have it. The barrier to entry for starting a business is practically zero. Building a website is so easy now. Back in the day it was either drop 5k+ on a developer or lose your mind learning code off YouTube. Today? You can launch a site in 20 minutes with zero code. The accessibility we have now is insane. Unlimited resources. Unlimited knowledge. No reason not to be making consistent income online. Here’s a look back at a few sites / companies I’ve built throughout the years before AI was released to the public. Long before Atarnity existed…

Daniella Dena

42,453 次观看 • 2 个月前

On your open perpetual position can become an NFT and live in your wallet. You open a position like normal than mint it. The program issues a token 2022 nft, that points to your exact position. Normally your open position lives inside the protocol. With Percolator Trade your able to wrap it into an NFT that sits in your own wallet. A real, ownable object instead of a line in a dashboard. Because it’s a token, it moves like one. Send the NFT to another wallet and the position goes with it. Whoever holds the NFT owns the trade. One NFT = one position If you burn it, your back to holding the position directly. That’s the composable part, a position use to be stuck inside one account. Because it’s a token, it can now be sold OTC or used by anything that speaks tokens. Why does it matter? Because the position is bound to the NFT, it can’t be drained out from under whoever holds it so there is no middleman that holds your position. The NFT does, and the NFT is yours. One important safety detail; transfers run through a token 2022 hook that checks the positions health first, so it can’t be handed off in a broken state & the NFT layer is a separate program so it can never touch the funds the core engine holds.

On your open perpetual position can become an NFT and live in your wallet. You open a position like normal than mint it. The program issues a token 2022 nft, that points to your exact position. Normally your open position lives inside the protocol. With Percolator Trade your able to wrap it into an NFT that sits in your own wallet. A real, ownable object instead of a line in a dashboard. Because it’s a token, it moves like one. Send the NFT to another wallet and the position goes with it. Whoever holds the NFT owns the trade. One NFT = one position If you burn it, your back to holding the position directly. That’s the composable part, a position use to be stuck inside one account. Because it’s a token, it can now be sold OTC or used by anything that speaks tokens. Why does it matter? Because the position is bound to the NFT, it can’t be drained out from under whoever holds it so there is no middleman that holds your position. The NFT does, and the NFT is yours. One important safety detail; transfers run through a token 2022 hook that checks the positions health first, so it can’t be handed off in a broken state & the NFT layer is a separate program so it can never touch the funds the core engine holds.

Percolator

12,359 次观看 • 3 个月前

Look what they put on my hamburger! 🍔 Young children aren’t the only ones who make sense of the world by connecting new experiences with prior knowledge - it’s just that there are so many new experiences to be had at their age. Take this little one, who has opened her cheeseburger only to find that it is unexpectedly covered with small, white, rectangular objects. Drawing on her prior knowledge of objects matching this same description, she makes a well-reasoned (if inadvertently hilarious) inference: someone at the restaurant has covered her burger with TEETH! 🦷 🦷 🦷 What an amazing window on her thinking. In this case she’ll have to accommodate for some new learning today: adding onions to her mental inventory of small, white, rectangles. But for now her reasoning is more than just funny. It’s really quite clever. Has your child ever encountered something new, only to mistake it for something to which they’d already been exposed? I’d love to hear your stories! 🎥 This amazing video - and genius little learner - was shared on TT by brandywilson566.

Look what they put on my hamburger! 🍔 Young children aren’t the only ones who make sense of the world by connecting new experiences with prior knowledge - it’s just that there are so many new experiences to be had at their age. Take this little one, who has opened her cheeseburger only to find that it is unexpectedly covered with small, white, rectangular objects. Drawing on her prior knowledge of objects matching this same description, she makes a well-reasoned (if inadvertently hilarious) inference: someone at the restaurant has covered her burger with TEETH! 🦷 🦷 🦷 What an amazing window on her thinking. In this case she’ll have to accommodate for some new learning today: adding onions to her mental inventory of small, white, rectangles. But for now her reasoning is more than just funny. It’s really quite clever. Has your child ever encountered something new, only to mistake it for something to which they’d already been exposed? I’d love to hear your stories! 🎥 This amazing video - and genius little learner - was shared on TT by brandywilson566.

Dan Wuori

300,281 次观看 • 3 年前

The studio is open. Creating digital humans used to cost a fortune. Higgsfield AI 🧩 has opened the AI Influencer Studio to everyone. Now it is free for everyone. You have access to powerful tools. It combines: ↓ Design ↓ Motion Storytelling is built in. Bring your idea. Leave with an influencer. Start building today. #Higgsfield #AIInfluencerStudio #CreatorEconomy

The studio is open. Creating digital humans used to cost a fortune. Higgsfield AI 🧩 has opened the AI Influencer Studio to everyone. Now it is free for everyone. You have access to powerful tools. It combines: ↓ Design ↓ Motion Storytelling is built in. Bring your idea. Leave with an influencer. Start building today. #Higgsfield #AIInfluencerStudio #CreatorEconomy

Himanshu Kumar

91,464 次观看 • 8 个月前

🚨 Lue Elizondo Drops Terrifying Warning – “Knowledge So Earth-Shattering It Could Threaten The Whole Of Humanity” 😱🛸👽 In this powerful Curt Jaimungal interview, Lue Elizondo asks the chilling question: “What if there is a knowledge that is so earth shattering that it could predicate an event that could threaten the whole species?” Is he referring to something inside the classified UAP programs that’s so dangerous, governments are afraid to release it. What do YOU think this forbidden knowledge actually is? An alien invasion plan? Proof we’re not at the top of the food chain? Something about consciousness or our true origins? And do you believe Lue Elizondo when he says this? Be honest — drop your theories below! #UFO #Disclosure

🚨 Lue Elizondo Drops Terrifying Warning – “Knowledge So Earth-Shattering It Could Threaten The Whole Of Humanity” 😱🛸👽 In this powerful Curt Jaimungal interview, Lue Elizondo asks the chilling question: “What if there is a knowledge that is so earth shattering that it could predicate an event that could threaten the whole species?” Is he referring to something inside the classified UAP programs that’s so dangerous, governments are afraid to release it. What do YOU think this forbidden knowledge actually is? An alien invasion plan? Proof we’re not at the top of the food chain? Something about consciousness or our true origins? And do you believe Lue Elizondo when he says this? Be honest — drop your theories below! #UFO #Disclosure

Interstellar

52,164 次观看 • 7 个月前

Introducing: OpenGranola 🔥 I built an open source meeting copilot for macOS. It transcribes both sides of your call on-device, searches your own notes in real time, and hands you talking points right when the conversation needs them. No audio leaves your Mac. Point it at a folder of markdown files, pick any LLM through OpenRouter (Claude, GPT-4o, Gemini, Llama), and it just works. It's invisible to screen share too — nobody knows you have it. The whole thing is open source. Link below

Introducing: OpenGranola 🔥 I built an open source meeting copilot for macOS. It transcribes both sides of your call on-device, searches your own notes in real time, and hands you talking points right when the conversation needs them. No audio leaves your Mac. Point it at a folder of markdown files, pick any LLM through OpenRouter (Claude, GPT-4o, Gemini, Llama), and it just works. It's invisible to screen share too — nobody knows you have it. The whole thing is open source. Link below

yazin

293,571 次观看 • 6 个月前