What if you could audit your AI: Why the Future of AI Needs a Little Certainty with Its Swagger linkedin.com/pulse/why-futu… via @LinkedIn
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Leonata
272 posts
Leonata
@AmyfromLeonata
Co-founder Leonata Built Software that doesn’t talk back, leak data, or need the cloud. Offline. Private. Sharp. Because not everything needs 70B parameter ego
San Francisco Katılım Mart 2011
262 Takip Edilen82 Takipçiler
RAG guesses. Leonata knows. linkedin.com/pulse/rag-gues… via @LinkedIn
#RAG #vectorgraph #AI #knowledgegraphs
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Most AI Products Won’t Pay Off. linkedin.com/pulse/most-ai-… via @LinkedIn #mining #projectengineer #RiskManagement
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Budget Blowouts Don’t Come From Bad Decisions linkedin.com/pulse/budget-b… via @LinkedIn
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Kindness for strangers is image management…#kindness
De Christian Life@DeChristianLife
True Kindness is to your household
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Leonata retweetledi
Beyond the Hype: How Leonata Solves the Language Problem Thats Been Breaking Search for 50 Years linkedin.com/pulse/beyond-h… via @LinkedIn #entityextraction #rag #ai #Knowledgegraph
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Beyond Memory: Why Knowledge Graphs Should Think, Not Just Retrieve...(good dog)
It's finally a popular topic #trending
linkedin.com/pulse/beyond-m… #AI #knowledgegraphs
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facebook.com/share/v/19i9mj…
We’re doing what we love. Are you doing what you love?
#knowledgegraph #ai #academic
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Personal brand time! Let’s go. I launched a little insta last night all things Farm, Neuro spicy and AI what a mix huh?
#farmlife #ai #personalbrand #amytalkstech
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@MushtaqBilalPhD Would @Leximancer be helpful here to do automatics lit reviews?
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Univeristy of Melbourne Library's comprehensive guide on what different types of literature reviews are and what they entail.
Totally free and includes a bunch of sources for further reading.
unimelb.libguides.com/whichreview/ho…
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Workforce Planning - what if you could predict the future? linkedin.com/pulse/workforc… #workforceplanning #HRsoftware #predictionmodels #ai
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excited to try
Maryam Miradi, PhD@MaryamMiradi
🛠️🧭 How to Build AI Agents from Scratch – Even If You’ve Never Done It Before This is 9 Step roadmap from prompt to UI. 》Step 1: Define the Agent’s Role and Goal ✸ What will your agent do? ✸ Who is it helping? ✸ What kind of output will it generate? → Example: A medical assistant agent that reads X-rays, summarizes findings, and speaks results. 》Step 2: Design Structured Input & Output ✸ Use Pydantic AI or JSON Schemas to define what the agent receives and returns. ✸ Avoid messy text — think like an API. → Tool: Pydantic AI, LangChain Output Parsers 》Step 3: Prompt and Tune the Agent’s Behavior ✸ Start with role-based system prompts ✸ Use Prompt Tuning or Prefix Tuning for consistent persona and task behavior → Tools: GPT-4, Claude, Prefix Tuning, Prompt Tuning 》Step 4: Add Reasoning and Tool Use ✸ Equip the agent with reasoning frameworks: ☆ ReAct (Reasoning + Action) ☆ Chain-of-Thought ✸ Allow access to tools like web search, code interpreters, or document retrievers. → Tools: LangChain, OpenAI Tools, ReAct Framework 》Step 5: Structure Multi-Agent Logic (if needed) ✸ Use orchestration frameworks to define agent roles and coordination. ✸ Create Planner, Researcher, Reporter agents — each with its own input/output schema. → Tools: CrewAI, LangGraph, OpenAI Swarm 》Step 6: Add Memory and Long-Term Context ✸ Does your agent need to remember what happened earlier? ✸ Use conversational memory, summary memory, or vector-based memory. → Tools: Zep, LangChain Memory, Chroma 》Step 7: Add Voice or Vision Capabilities (Optional) ✸ Text-to-speech: Use Coqui or ElevenLabs ✸ Image understanding: Use GPT-4o or LLaMA 3.2 Vision → Let your agent see and speak. 》Step 8: Deliver the Output (in Human or Machine Format) ✸ Format outputs into Markdown → PDF or structured JSON ✸ Output must be both readable and parsable → Tools: Pydantic AI, Markdown-to-PDF, LangChain Output Parsers 》Step 9: Wrap in a UI or API (Optional) ✸ Create a front-end or expose your agent via API ✸ Use Gradio, Streamlit, or FastAPI → This is what turns your agent into a product. ≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣ ⫸ꆛ Want to build Real-World AI agents? Join My 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝟰-𝗶𝗻-𝟭 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 TODAY! ➠ Build Real-World AI Agents for Healthcare, Finance, Aviation, Smart Cities ➠ Learn 4 Framework: LangGraph | PydanticAI | CrewAI | OpenAI Swarm ➠ More Tools: Hugging Face, Foloim, ElevenLabs, Gradio and more ➠ Work with Text, Audio, Video and Tabular Data 👉𝗘𝗻𝗿𝗼𝗹𝗹 𝗡𝗢𝗪 (𝟰𝟱% 𝗱𝗶𝘀𝗰𝗼𝘂𝗻𝘁): maryammiradi.com/ai-agents-mast…
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Climate Adaptation as Trajectories: A New Way to Track Resilience in Motion by Leonata linkedin.com/pulse/climate-… via @LinkedIn
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LLMs Don’t Think. They Fetch. Here's What Real Reasoning Looks Like...
linkedin.com/pulse/beyond-m… via @LinkedIn
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I love a good lie...leonata can't lie, if the truth matters try it...
Mushtaq Bilal, PhD@MushtaqBilalPhD
Avoid using ChatGPT for academic work. It's newer models hallucinate even more. Instead, try Bohrium: an AI app that integrates Deep Seek with multiple academic databases. Answers your questions with references to published papers and lets you chat with papers too – for free
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Hoping you see this @sama I have deterministic ai that I think would solve many of your scaling and learning issues.
NodeRAG and Leonata come at the graph-based reasoning problem from radically different angles:
》NodeRAG: Graph as Enriched Memory
✸ Persistent Heterograph: NodeRAG pre-processes a corpus into a reusable heterograph, storing structured representations of semantic units, relationships, summaries, and more.
✸ LLM-enhanced Indexing: It uses LLMs to build the graph, but at query time, retrieval is powered by shallow Personalized PageRank and dual search (vector + symbolic), not re-generation.
✸ Retrieval-optimized: Its focus is on low-latency, high-precision retrieval for multi-hop QA and LLM augmentation. It’s like giving LLMs a structured, query-friendly brain.
》Leonata: Graph as Dynamic Reasoner
✸ Query-Time Graph Construction: Instead of indexing a corpus, Leonata builds a brand new knowledge graph on the fly for every query.
✸ No LLM, No Embeddings: This is striking—it means it’s operating with deterministic graph logic rather than probabilistic language modeling.
✸ Built-in Ontological Reasoning: From what I’ve seen, it’s closer to symbolic AI, where logical consistency, ontological constraints, and explainability are first-class citizens.
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we really do try to listen to feedback!
we would love to be able to do even more; we continue to have to make very hard tradeoffs between rate limits, new feature launches, and latency.
the GPUs are coming, so hopefully it gets better.
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we have doubled rate limits for o3 and o4-mini-high for chatgpt plus subscribers.
enjoy!
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@ajeetunc Here is what I'm building youtu.be/B96uI0mnXKo #buildinpublic #mvp #artificialintelligence #betterthanrag
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