Leonata

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

[Leonata @AmyfromLeonata]

What if you could audit your AI: Why the Future of AI Needs a Little Certainty with Its Swagger https://t.co/dxrcOa0Deu via @LinkedIn

[Leonata @AmyfromLeonata]

RAG guesses. Leonata knows. https://t.co/uA8Qbuc03r via @LinkedIn
#RAG #vectorgraph #AI #knowledgegraphs

[Leonata @AmyfromLeonata]

https://t.co/OSBvLvPr0w
New video

[Leonata @AmyfromLeonata]

Most AI Products Won’t Pay Off. https://t.co/pfxiMVNEmR via @LinkedIn #mining #projectengineer #RiskManagement

[Leonata @AmyfromLeonata]

Budget Blowouts Don’t Come From Bad Decisions https://t.co/qGtOKaFg94 via @LinkedIn

[Leonata @AmyfromLeonata]

Kindness for strangers is image management…#kindness

[De Christian Life @DeChristianLife]

True Kindness is to your household

[De Christian Life @DeChristianLife]

True Kindness is to your household

[Leonata @AmyfromLeonata]

Beyond the Hype: How Leonata Solves the Language Problem Thats Been Breaking Search for 50 Years https://t.co/3CNtjGSD5V via @LinkedIn #entityextraction #rag #ai #Knowledgegraph

[Leonata @AmyfromLeonata]

https://t.co/5Zof20XFzF

[Leonata @AmyfromLeonata]

Beyond Memory: Why Knowledge Graphs Should Think, Not Just Retrieve...(good dog)

It's finally a popular topic #trending

https://t.co/81AJwKsUmR #AI #knowledgegraphs

[Leonata @AmyfromLeonata]

https://t.co/4sEuWcOucO

We’re doing what we love. Are you doing what you love?

#knowledgegraph #ai #academic

[Leonata @AmyfromLeonata]

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

[Leonata @AmyfromLeonata]

@MushtaqBilalPhD Would @Leximancer be helpful here to do automatics lit reviews?

[Leonata @AmyfromLeonata]

Workforce Planning - what if you could predict the future? https://t.co/szC6TGBVex #workforceplanning #HRsoftware #predictionmodels #ai

[Leonata @AmyfromLeonata]

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.

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⫸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):
https://t.co/5i2v1fIrhJ

[Leonata @AmyfromLeonata]

Climate Adaptation as Trajectories: A New Way to Track Resilience in Motion by Leonata https://t.co/c8UOEM5jQz via @LinkedIn

[Leonata @AmyfromLeonata]

LLMs Don’t Think. They Fetch. Here's What Real Reasoning Looks Like...
https://t.co/358w4HxAAp via @LinkedIn

[Leonata @AmyfromLeonata]

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

[Leonata @AmyfromLeonata]

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.

[Leonata @AmyfromLeonata]

@ajeetunc Here is what I'm building https://t.co/4woyJN91MD #buildinpublic #mvp #artificialintelligence #betterthanrag