How to build an LLM memory system with DSPy and Mem0 API

I recently published "Inside nanochat Part 4: Understanding Inference". After covering tokenization, architecture, and optimizers in earlier parts, this post explains how nanochat generates text efficiently with KV caching, tool integration, and smart sampling strategies. You can read it here: https://lnkd.in/dETJjRPb If you missed the earlier parts: part 1 - tokenization: https://lnkd.in/dW4CNQhU part 2 - architecture: https://lnkd.in/dzMk6fv2 part 3 - optimizers: https://lnkd.in/dK__mJed

Launched a new free course on BilberryDB Hub: Reinforcement Learning from Human Feedback: The Complete Mathematical Walkthrough. It’s fully focused on the mathematical walkthrough, with a little bit of theoretical concept. Course link: https://lnkd.in/eCg4aPQa Key topics covered: Reward Model Training, Bradley-Terry Model and Cross-Entropy Derivation, PPO Optimization, Sampling Actions and Generating Responses, Reward and KL Penalty, Generalized Advantage Estimation, and more.. [1] Architecture source: https://lnkd.in/e7fwtAZG

Stanford CS336: Large Language Models from Scratch! This is a comprehensive course on LLMs, covers the full process of building one from scratch, including data collection, pretraining, transformer architecture, training, evaluation, and deployment. After going through it in detail, here’s why it’s different: → Covers foundations, tokenization, and PyTorch from the ground up → Breaks down architectures, hyperparameters, and mixture of experts → Demystifies GPUs, kernels, and Triton → Explains parallelism and scaling laws for training at scale → Walks through inference, evaluation, STF, and RLHF for deployment and alignment Link to the full playlist in the comments!

Part 5 of the "Inside nanochat" series explores the full training pipeline: tokenizer training, pretraining, midtraining, fine-tuning, and reinforcement learning. You can read it here: https://lnkd.in/diQuQQGX If you missed the earlier parts: part 1 - tokenization: https://lnkd.in/dW4CNQhU part 2 - architecture: https://lnkd.in/dzMk6fv2 part 3 - optimizers: https://lnkd.in/dK__mJed part 4 - inference: https://lnkd.in/dETJjRPb

- Built-in Retry mechanisms - Composable architectures - Graph-based strategies - Automatic Roll-back to earlier checkpoints - Knowledge retrieval and memory - History adaptations & context compression - OpenTelemetry exporters for smart observability Definitely up there, with Embabel. Both breaking new ground, and laying the foundations for innovative agentic frameworks for the JVM (IMO) Koog >> https://lnkd.in/eyq9VtfG #LookUp #AgenticAI #JVM #AgenticInnovations

We are adding transport interceptors to our plugins architecture. This means that when you write plugins for Bifrost by Maxim AI, you can intercept at the protocol layer (HTTP/gRPC) or at the inference call level (model input and model output). Tune the gateway the way you want! :) Start here: https://getmax.im/bifrost

𝗜𝗳 𝘆𝗼𝘂 𝘂𝘀𝗲 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗼𝗿 𝗱𝗼𝗰𝗸𝗶𝗻𝗴, 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗳𝗼𝗿 𝘆𝗼𝘂. 😁 𝗧𝗟𝗗𝗥: After you predict a complex, run DockQ. pLDDT isn’t an interface score, so use DockQ before you trust a pose. AlphaFold is great, but high pLDDT is not an interface score. It says the local backbone looks confident. It does not tell you if two chains are actually making the right contacts. For that you need something else. Picture this: you dock an antibody to a target. The model looks clean, pLDDT is green, figures are pretty. But CDRH3 (antibody binding part) is skating past the epitope and the paratope residues. Wet lab fails and you blame docking. Now sort your poses by DockQ. A different model jumps to the top. The backbone shifts a little, the loops clasp the right patch, and a quick contact map shows the expected residues actually meet. That is the one you should take forward. DockQ is the interface sanity check. It compresses three signals into one 0-1 score you can rank on: • Fnat: are the right residues touching • iRMSD: how much the interface backbone moved • LRMSD: how far the ligand shifted after aligning on the receptor 𝗖𝗵𝗲𝗰𝗸 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗯𝗹𝗼𝗴: https://lnkd.in/epi4NHqd 𝗥𝘂𝗻 𝘆𝗼𝘂𝗿 𝗳𝗶𝗿𝘀𝘁 𝗗𝗼𝗰𝗸𝗤 𝗷𝗼𝗯 𝗼𝗻 Vici.bio: https://lnkd.in/eZDisvnq 𝗧𝗲𝗹𝗹 𝘂𝘀 𝘄𝗵𝗮𝘁'𝘀 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗼𝗿 𝗽𝗮𝗿𝘁𝗻𝗲𝗿:  info@vici.bio 𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀: https://lnkd.in/e7inuiy3 https://lnkd.in/egCqN3Bp

I didn’t hear a lot of people talking about this paper or maybe I missed something. This discussed an important concept regarding transformer architecture where instead of just predicting the next token, an attention or an emphasis is put on into an intermediate step. An encoder has been added to target when a generation is happening in a way that is learning what is the meaning of previous state ( pred - 1) . That is changing everything.

A great lesson in algorithm engineering by Tobias Ziegler of TigerBeetle in his talk on Taming the Tail Latency at #P99CONF by #ScyllaDB. He started with algorithm selection for merging the sorted runs in TigerBeetleDB, where he chose the basic radix sort and min-heap and finally refined it to use the "tree of losers" algorithm. Then he followed it up with some awesome algorithm engineering to improve the performance .. awesome talk Tobias!

What is URIEL_AI? URIEL is a self-updating factual engine that routes, retrieves, stores, and verifies knowledge with calibrated confidence and metacognitive checks. It’s built to minimize hallucinations, keep provenance, and improve itself safely as it ingests more public information. Why? Teams need reliable, continuously-learning assistants over their docs + the public web, but generic LLMs hallucinate and forget. URIEL is an architecture for memory + routing + verification, and auditable at every step.