Range-Based Set Reconciliation via Range-Summarizable Order-Statistics Stores

What’s Wrong with Synthetic Data for Scene Text Recognition? A Strong Synthetic Engine with Diverse Simulations and Self-Evolution

LwMQ Storage API — LwMQ 1.0 documentation

Why I Used SQLite for a 250GB Production Database | Anyim Ossi — AI Software Engineer

An OSINT lookup tool on 250GB of scraped public data. SQLite, not Postgres — because eliminating IPC overhead mattered more than query planner sophistication. Query times fell from 4 hours to 40ms across six optimization layers.

LwMQ IPC Messaging System Performance | Axel Rietschin posted on the topic | LinkedIn

Finally, some end-to-end numbers with actual messages across processes on the same box. Long story short: the message rate for tiny messages (<= 80 bytes) is above 10 million/sec, while larger messages (4KB in this test) are not as fast at ~2M msg/sec, but the data throughput climbs above 70Gbps, all on a 2024 gaming laptop. The client (sender) seems faster than the server (receiver) because what is measured really is the message creation + queuing. Since LwMQ is fully asynchronous, the client is done queuing before all messages are actually sent over the underlying transport. I have yet to implement a lingering mechanism to allow for draining the send queue(s) before closing. The numbers don't necessarily reflect the last word on the subject. Note that the address is formulated using a familiar Uri format. However, no network component is involved in this IPC communication, and the hostname and port are merely illustrative. Any string can be used. Only point-to-point (1:1) channels are supported at this time, so a "server" must open a separate channel with each "client" it talks to. Small but important point: it does not matter which side starts first. A "client" can start, open a channel, and begin queuing messages long before anyone is listening, or vice versa. LwMQ connects in the background and delivers messages when a link is established, without losses. This relaxes the dreaded start-order dependencies found in pretty much every other IPC mechanism under the sun. Messages are structured entities with one or more data frames and optional timestamps, and the times include the creation and disposal of all messages. The callers can create multiple send queues per channel. There are various types of queues, single and multi-producer, bounded or not (where a queue blocks when reaching a given capacity) or discarding (where a queue discards either the oldest or the newest message when full) and last but not least, a tagged queue where messages can be assigned an optional tag (type) and only the last message of a given type can exist in the queue at any given time. This enables advanced scenarios. Oh, and priorities: queues have five standard priorities, plus idle and time-critical, which governs how the channel thread services them. This is the most sophisticated and flexible queuing system I'm aware of, and it should cover many edge scenarios from heartbeat to batching graphics commands to tracelogging. In the works: RDMA, TCP/RIO, and HvSocket transports. Stay tuned. www.lwmq.net - Your next favorite IPC messaging system covering everything from AI training workloads to financial data to run-of-the-mill IPC messages, as fast as hardware allows. Fun fact: the messaging DLL currently weighs 323KB, and the whole platform, including a super fast in-memory cache, a new persistent KV store based on LMDB, and the utility libraries for file cleaning, hashing, and more, weighs about 3.5MB total, or about the size of a "Hello World" in Rust 😉

SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction

Virtual cell models aim to enable in silico experimentation by predicting how cells respond to genetic, chemical, or cytokine perturbations from single-cell measurements. In practice, however, large-scale perturbation prediction remains constrained by three coupled bottlenecks: inefficient training and inference pipelines, unstable modeling in high-dimensional sparse expression space, and evaluation protocols that overemphasize reconstruction-like accuracy while underestimating biological fidelity. In this work we present a specialized large-scale foundation model SCALE for virtual cell perturbation prediction that addresses the above limitations jointly. First, we build a BioNeMo-based training and inference framework that substantially improves data throughput, distributed scalability, and deployment efficiency, yielding 12.51* speedup on pretrain and 1.29* on inference over the prior SOTA pipeline under matched system settings. Second, we formulate perturbation prediction as conditional transport and implement it with a set-aware flow architecture that couples LLaMA-based cellular encoding with endpoint-oriented supervision. This design yields more stable training and stronger recovery of perturbation effects. Third, we evaluate the model on Tahoe-100M using a rigorous cell-level protocol centered on biologically meaningful metrics rather than reconstruction alone. On this benchmark, our model improves PDCorr by 12.02% and DE Overlap by 10.66% over STATE. Together, these results suggest that advancing virtual cells requires not only better generative objectives, but also the co-design of scalable infrastructure, stable transport modeling, and biologically faithful evaluation.

Ever-Lmdb-Sdk - Evernode Smart Contract DB.

Securly store and retrieve data in evernode smart contracts using lmdb