Vanessa Sabino

If you’re building with LLMs, these are 10 toolkits I highly recommend getting familiar with 👇 Whether you’re an engineer, researcher, PM, or infra lead, these tools are shaping how GenAI systems get built, debugged, fine-tuned, and scaled today. They form the core of production-grade AI, across RAG, agents, multimodal, evaluation, and more. → AI-Native IDEs (Cursor, JetBrains Junie, Copilot X) Modern IDEs now embed LLMs to accelerate coding, testing, and debugging. They go beyond autocomplete, understanding repo structure, generating unit tests, and optimizing workflows. → Multi-Agent Frameworks (CrewAI, AutoGen, LangGraph) Useful when one model isn’t enough. These frameworks let you build role-based agents (e.g. planner, retriever, coder) that collaborate and coordinate across complex tasks. → Inference Engines (Fireworks AI, vLLM, TGI) Designed for high-throughput, low-latency LLM serving. They handle open models, fine-tuned variants, and multimodal inputs, essential for scaling to production. → Data Frameworks for RAG (LlamaIndex, Haystack, RAGflow) Builds the bridge between your data and the LLM. These frameworks handle parsing, chunking, retrieval, and indexing to ground model outputs in enterprise knowledge. → Vector Databases (Pinecone, Weaviate, Qdrant, Chroma) Backbone of semantic search. They store embeddings and power retrieval in RAG, recommendations, and memory systems using fast nearest-neighbor algorithms. → Evaluation & Benchmarking (Fireworks AI Eval Protocol, Ragas, TruLens) Lets you test for accuracy, hallucinations, regressions, and preference alignment. Core to validating model behavior across prompts, versions, or fine-tuning runs. → Memory Systems (MEM-0, LangChain Memory, Milvus Hybrid) Enables agents to retain past interactions. Useful for building persistent assistants, session-aware tools, and long-term personalized workflows. → Agent Observability (LangSmith, HoneyHive, Arize AI Phoenix) Debugging LLM chains is non-trivial. These tools surface traces, logs, and step-by-step reasoning so you can inspect and iterate with confidence. → Fine-Tuning & Reward Stacks (PEFT, LoRA, Fireworks AI RLHF/RLVR) Supports adapting base models efficiently or aligning behavior using reward models. Great for domain tuning, personalization, and safety alignment. → Multimodal Toolkits (CLIP, BLIP-2, Florence-2, GPT-4o APIs) Text is just one modality. These toolkits let you build agents that understand images, audio, and video, enabling richer input/output capabilities. If you're deep in AI infra or systems, print this out, build a test project around each, and experiment with how they fit together. You’ll learn more in a weekend with these tools than from hours of reading docs. What’s one tool you’d add to this list? 👇 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI infrastructure insights, and subscribe to my newsletter for deeper technical breakdowns: 🔗 https://lnkd.in/dpBNr6Jg

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Today, DeepSeek AI released a new open-source OCR model that’s been making waves on X. At Pulse, we tested it on real-world documents like financial statements, handwritten forms, and nested tables. The results were interesting but inconsistent. The same challenges we’ve seen with other LLM-driven OCR systems showed up again: unstable outputs, hallucinated text, and unreliable structure preservation that make enterprise-scale deployment difficult. Let’s put that into context. With 1,000 pages containing 200 extracted fields each, even a 99 % accuracy rate means 2,000 incorrect entries. When precision is critical, that’s the difference between a trusted data pipeline and a broken one. Our tests revealed: • Bounding boxes that shifted across runs • Handwriting misreads and hallucinated text • Table structures that failed to render correctly Pulse’s approach combines proprietary table-transformer models with classical vision algorithms, using LLMs only where they excel. The result is near-perfect accuracy, full data preservation, and low-latency processing at scale. Want to chat? Send a message directly or use the link in the comments.

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I manage support, AI training, onboarding, QA, product design, testing, and a half dozen other things at RB2B. Here's what's in my toolkit. ➜ Intercom is the mission control. I triage tickets, train the bot, push proactive nudges, and manage live chats; all from one screen. It's the pulse of RB2B support. ➜ Lovable saves me from dev backlogs. We're using it to design V3 of RB2B: mapping features, user flows, and UI without writing code. It's where ideas become functional blueprints. ➜ Supademo kills friction. Instead of hand-holding new users, I ship interactive product tours that onboard at scale. One demo replaces 10 DMs. ➜ Upvoty is my feedback brain. Every upvote, comment, and request tells me what to build next. It's how I turn user pain into AI workflows and messaging plays. ➜ Postman keeps integrations honest. When integration API calls go sideways for a user, Postman helps me take a look under the hood and find out why. ➜ Tolt runs our referral engine. Tracks referrals, manages payouts, and keeps our growth loop running without manual oversight. ➜ ChatGPT is my daily editor. Process breakdowns, copy rewrites, bot response ideas; this is where they start. GPT gives me speed. Not fluff. ➜ Claude brings tone balance. When I want a softer angle or a second take on phrasing, Claude is where I go. The contrast helps sharpen our messaging. ➜ n8n for behind the scenes workflows. We use it to link our AI agent to our ESP. So, if an email bounces, the bot knows before support ever sees a ticket. ➜ NewRelic and Logz.io are my panic buttons. If something breaks, I need answers fast. These tools tell me where the fire started. And whether it’s still burning. Anyone have an inside line on a tool that I should be using but aren't?

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Proud to share our first research paper: we've completed the first large-scale validation of an autonomous AI doctor in real clinical practice. Our study compared Doctronic's AI with board-certified clinicians across 500 urgent care encounters. The results? 81% diagnostic concordance and 99.2% treatment plan alignment, with zero clinical hallucinations. What struck me most: when experts reviewed discordant cases, AI outperformed humans ≈36% of the time versus ≈9% the other way. Not because AI replaces clinical judgment, but because it synthesizes vast medical knowledge consistently. This is a preprint but it seems that at Doctronic we're on the right path to address the projected shortage of 11 million healthcare practitioners by 2030. Technology that raises the floor of healthcare isn't just possible - it's here. Read the full study: https://lnkd.in/e3vUhsNs #HealthTech #DigitalHealth

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