# precision Published articles for precision. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## HP EliteBook 8470p and Dell Precision T1650 support added to Libreboot DevFeed: [HP EliteBook 8470p and Dell Precision T1650 support added to Libreboot]() Original publisher: [Read original article]() Author: Leah Rowe Published: 2026-09-17T04:32:50.666044Z Content type: news Language: en Sources: [News about Libreboot releases and development]() Topics: [libreboot](), [dell](), [coreboot](), [intel](), [Linux](), [Debian](), [Nvidia]() Tags: [article](), [bios](), [canoeboot](), [coreboot](), [debian](), [dell](), [free-software](), [intel](), [libre](), [libreboot](), [linux](), [news](), [nvidia](), [opensource](), [precision](), [uefi]() ### AI overview Libreboot added support for the HP EliteBook 8470p and Dell Precision T1650. The article describes tested hardware variants, installation guidance, graphics limitations, boot configuration, and the T1650's ECC memory support. ### Source excerpt Article: HP EliteBook 8470p and Dell Precision T1650 support added to Libreboot Web link: https://libreboot.org/news/hp8470p_and_dell_t1650.html ## Build a serverless PII redaction pipeline with Amazon Bedrock Data Automation DevFeed: [Build a serverless PII redaction pipeline with Amazon Bedrock Data Automation]() Original publisher: [Read original article]() Author: Samantha Stuart Published: 2026-09-16T15:17:37Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock](), [pii](), [Serverless](), [AWS Lambda](), [AWS Step Functions](), [Artificial Intelligence](), [Amazon Web Services](), [Ansible]() Tags: [advanced-300](), [amazon-bedrock](), [amazon-bedrock-data-automation](), [aws](), [aws-lambda](), [aws-step-functions](), [how-to](), [pii-redaction](), [pipeline](), [precision](), [serverless](), [technical-how-to]() ### AI overview This tutorial presents a serverless AWS pipeline for detecting and redacting personally identifiable information in scanned documents and images. It uses Amazon Bedrock Data Automation with a custom blueprint, AWS Step Functions, and AWS Lambda, with a token-matching quality check to improve recall on degraded and handwritten documents. ### Source excerpt Learn how to automate end-to-end PII detection and redaction from scanned documents at scale using Amazon Bedrock Data Automation with a custom blueprint, AWS Step Functions, and AWS Lambda. A custom blueprint redacts sensitive fields with field-level precision, and a token matching quality check raises recall across degraded and handwritten documents. ## New AI technique could make minimally invasive surgeries safer and more precise DevFeed: [New AI technique could make minimally invasive surgeries safer and more precise]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-09-16T15:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [AI Research](), [3D](), [navigation]() Tags: [3d](), [ai](), [artificial-intelligence](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [computer-vision](), [electrical-engineering-and-computer-science-eecs](), [health-care](), [images](), [imaging](), [jameel-clinic](), [machine-learning](), [medical-devices](), [medical-imaging](), [minimally-invasive-surgery](), [mit-ibm-computing-research-lab](), [mit-schwarzman-college-of-computing](), [model](), [national-institutes-of-health-nih](), [navigation](), [paper](), [polina-golland](), [precision](), [real-time](), [research](), [school-of-engineering](), [vision](), [vivek-gopalakrishnan]() ### AI overview MIT researchers and collaborators developed xvr, an AI method that adapts to individual patients and rapidly aligns intraoperative X-rays with preoperative 3D medical scans. The technique is intended to improve surgical navigation for minimally invasive procedures. ### Source excerpt This patient-specific method, called xvr, helps doctors use X-rays for surgical navigation in fields such as orthopedics and neurosurgery. ## What's New in PHP 8.6 DevFeed: [What's New in PHP 8.6]() Original publisher: [Read original article]() Author: Paul Redmond Published: 2026-09-15T03:00:09Z Content type: release Language: en Sources: [Laravel]() Topics: [PHP](), [DateTime](), [ISO 8601]() Tags: [2026](), [closure](), [feature](), [function](), [interface](), [iso](), [news](), [php](), [php-8-6](), [precision](), [properties](), [release]() ### AI overview An overview of PHP 8.6, scheduled for release on November 19, 2026. The article covers partial function application, the clamp() function, a nanosecond-precision Duration class, readonly property defaults, and parameter DocComments, along with the release timeline and related changes. ### Source excerpt PHP 8.6 arrives November 19, 2026 with partial function application, a clamp() function, a Duration class, readonly property defaults, and new deprecations. The post What's New in PHP 8.6 appeared first on Laravel News. Join the Laravel Newsletter to get Laravel articles like this directly in your inbox. ## LightMake L4 3D printer features four independent heads for simultaneous or multi-color printing (Crowdfunding) DevFeed: [LightMake L4 3D printer features four independent heads for simultaneous or multi-color printing (Crowdfunding)]() Original publisher: [Read original article]() Author: Debashis Das Published: 2026-09-02T10:55:06Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [3D]() Tags: [3d-printer](), [3d-printing](), [artificial-intelligence-ai](), [camera](), [crowdfunding](), [diy](), [hardware](), [kickstarter](), [precision](), [production](), [video]() ### AI overview The LightMake L4 is a desktop FDM 3D printer with four independently controlled printheads and linear-motor motion. It supports simultaneous duplicate printing, multi-color and multi-material printing, and batch production, with claimed toolhead changes in one second, near-zero purge waste, motion precision of ±1µm, and speeds up to 500 mm/s. ### Source excerpt The LightMake L4 is a desktop 3D printer with four independent printing heads for high-throughput multi-color, multi-material, and batch production. It uses four independently controlled printheads and linear-motor motion instead of conventional belts, allowing users to print four identical models simultaneously, produce complex multi-color parts with minimal purge waste, or combine up to four materials in a single print. Typically, multi-color FDM 3D printers like the Bambu Lab X1 or Geeetech A20M use a single-nozzle purge material during every color change, which leads to a lot of material waste and longer print times. The LightMake L4 takes a different approach by keeping four filaments loaded and heated in four completely separate extruders. According to the company, this allows for one-second toolhead changes and near-zero purge waste. The linear motor system also promises ±1µm motion precision and speeds of up to 500 mm/s. LightMake L4 and L1 specifications: Printing technology - [...] The post LightMake L4 3D printer features four independent heads for simultaneous or multi-color printing (Crowdfunding) appeared first on CNX Software - Embedded Systems News. ## Makera Z1 Desktop CNC Review - Smart milling, 4-th axis module, and 5W laser engraving DevFeed: [Makera Z1 Desktop CNC Review - Smart milling, 4-th axis module, and 5W laser engraving]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-09-01T10:51:29Z Content type: article Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Tool]() Tags: [camera](), [cnc-router](), [diy](), [dust](), [hardware](), [optional](), [precision](), [review](), [reviews](), [testing](), [video](), [volume]() ### AI overview A review of the Makera Z1 desktop CNC machine, covering its enclosed aluminum construction, 200 x 200 x 100 mm work volume, milling and engraving capabilities, assistive features, specifications, and optional 4th-axis and 5W laser accessories. ### Source excerpt The Makera Z1 Desktop CNC is a desktop CNC machine designed for milling and engraving work that requires precision. The fully enclosed machine features an aluminum structure, offers a 200 x 200 x 100 mm work volume, and is suitable for wood, plastic, acrylic, PCB boards, carbon fiber, as well as non-ferrous metals such as aluminum, brass, and copper. The standout feature of this model is its fairly complete set of assistive functions, including a quick tool change system for swapping milling bits, auto probing & leveling for detecting position and leveling the workpiece, the AeroDust system for blowing and managing chips/dust, and a built-in camera for monitoring operation. In addition, optional accessories such as a 4th-axis rotary module, a 5W laser head, a workholding kit, and a 3D probe can also be installed. This model relies on a linear rail system featuring Acme lead screws and NEMA 17 stepper [...] The post Makera Z1 Desktop CNC Review - Smart milling, 4-th axis module, and 5W laser engraving appeared first on CNX Software - Embedded Systems News. ## Hot Chips 2026: Samsung's Processing-in-Memory (PIM) DevFeed: [Hot Chips 2026: Samsung's Processing-in-Memory (PIM)]() Original publisher: [Read original article]() Author: Chester Lam Published: 2026-08-29T05:36:33Z Content type: article Language: en Sources: [Chips and Cheese]() Topics: [samsung](), [Latency]() Tags: [2026](), [compute](), [dram](), [latency](), [memory](), [model](), [operations](), [parallel](), [precision](), [samsung]() ### AI overview The article examines Samsung's LPDDR5X Processing-in-Memory implementation presented at Hot Chips 2026. It describes PIM blocks placed in each DRAM bank, enabling access to internal memory bandwidth and supporting MAC computations near the data. ### Source excerpt In-memory compute with LPDDR5X ## Weekly recommendations of articles and videos about programming, graphics, and computing DevFeed: [Weekly recommendations of articles and videos about programming, graphics, and computing]() Original publisher: [Read original article]() Author: Romain Guy Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Posts on Romain Guy]() Topics: [Programming](), [GPU](), [intel]() Tags: [android](), [article](), [articles](), [books](), [developer](), [fpu](), [gpu](), [graphics](), [intel](), [kotlin](), [links](), [precision](), [programming](), [videos]() ### AI overview A weekly roundup of recommended reading and viewing covering Intel's MMX, linear algebra, floating-point precision, Bézier curves, blackbody color fits, GPU memory behavior, and Game Boy Advance graphics modes. ### Source excerpt Here is a list of tings I read/watched/played/etc. this week that you might find interesting or enjoyable: Things to read Link to heading SIMD in the 90s -- Great article explaining what Intel's MMX was all about. Linear algebra done right -- Contains a lot of stuff that I feel like I have to relearn every few years. Double-double: 31 digits of precision without leaving the FPU -- I really, really like reading about floaing points :) Curvature Béziers -- Yet another fascinating article on Bézier curves, this time with a focus on curvature. Blackbody: 4 rational fits -- A set of analytical fits for blackbody colors, from 798 to 5772 K. I have used a common fit before, but these fits look more useful in some situations. What happens when a GPU reads memory -- More information about how GPUs work is always welcome. Things to watch Link to heading How the Game Boy Advance's graphics modes made it so advanced -- A tour of the GameBoy Advance's graphics modes. I had a blast programming for the GBA back then, I kind of miss this style of graphics programming. ## Introducing memory retention for agentic memory in OpenSearch DevFeed: [Introducing memory retention for agentic memory in OpenSearch]() Original publisher: [Read original article]() Author: Erfan Ballew Published: 2026-08-13T22:29:25Z Content type: article Language: en Sources: [OpenSearch]() Topics: [Amazon OpenSearch Service](), [AI Agent](), [audit trail](), [model-deployment]() Tags: [agentic](), [audit-trail](), [blog](), [compliance](), [cost](), [experimental](), [lifecycle](), [memory](), [opensearch](), [precision](), [retention](), [storage](), [technical]() ### AI overview This article explains OpenSearch 3.8's experimental memory retention feature for agentic memory. It describes age-based and count-based limits for different memory types, how policies prevent stale context and uncontrolled storage growth, and how to enable retention on an existing cluster. ### Source excerpt Learn how the memory retention policy in OpenSearch automatically manages the lifecycle of agentic memory, controlling storage growth while preserving specific memories. The post Introducing memory retention for agentic memory in OpenSearch appeared first on OpenSearch. ## Leveraging PyFixest for High-Cardinality Marketplace Modeling at Instacart DevFeed: [Leveraging PyFixest for High-Cardinality Marketplace Modeling at Instacart]() Original publisher: [Read original article]() Author: Benjamin Knight Published: 2026-06-29T16:06:24Z Content type: article Language: en Sources: [Instacart]() Topics: [benchmarking](), [math](), [Algorithms, Complexity](), [Software](), [Programming](), [Routing (disambiguation)]() Tags: [benchmarking](), [bias](), [cardinality](), [data-science](), [delivery](), [efficiency](), [estimator](), [fixed-effects-model](), [instacart](), [linear-regression](), [measurement](), [memory](), [precision](), [pyfixest](), [regression](), [routing](), [speed](), [statistics](), [techniques]() ### AI overview This Instacart article explains why ordinary least squares regression becomes computationally impractical for marketplace experiments with high-cardinality categories. It presents the mathematical basis for using Fixest and Pyfixest, discusses switchback experiment designs for addressing treatment spillover, and describes benchmarks comparing processing speed, memory efficiency, and estimator precision. ### Source excerpt Benjamin S. Knight Scaling Marketplace experiments requires specialized statistical techniques. We examine why standard ordinary least squares regression (OLS) becomes computationally intractable when controlling for high-cardinality categories. We then dive into the underlying math and demonstrate how modern packages -- specifically Fixest and Pyfixest -- bypass these limitations. We conclude by benchmarking these methods to show their real-world impact on processing speed, memory efficiency, and estimator precision. At Instacart we strive to give our customers access to all the fresh foods and ingredients that they would normally get from a trip to the grocery store, but without the hassle of driving, finding parking, waiting in line, etc. Instacart's Marketplace team is responsible for surfacing customers' orders to shoppers, aligning Instacart's delivery windows with shoppers' projected availabilities as efficiently as possible. This entails a careful balancing act. If we offer delivery windows that are sooner / more popular, then we risk overextending shoppers' ability to fulfill those orders on time. If we are too conservative in our delivery option offerings, then we risk losing potential orders. Accurately measuring the impact of changes in our batching and routing algorithms requires thoughtful experiment design and software. Better predictions of future demand / time-to-fulfill allow Instacart to offer more convenient delivery windows.Experimentation on Marketplace One of our primary concerns in Marketplace is treatment spillage. For example, if we adjust our batching algorithm and increase the rate at which multiple orders are combined into batches in Brooklyn and Queens, then we face a real risk of also influencing the rate of batch creation / completion in Staten Island, the Bronx, and Manhattan. In this case the treatment impacts the control group -- a classic source of measurement bias as a consequence of violating the Stable Unit Treatment Value Assumpt ## double, BigDecimal, or Fixed-Point? DevFeed: [double, BigDecimal, or Fixed-Point?]() Original publisher: [Read original article]() Author: Stefano Fago Published: 2026-06-14T00:00:00Z Content type: article Language: en Sources: [Nicolas Fränkel]() Topics: [Java]() Tags: [bigdecimal](), [engineering](), [floating-point](), [java](), [numbers](), [performance](), [pitfalls](), [precision]() ### AI overview A Java-focused article explains how to choose among double, BigDecimal, and fixed-point arithmetic based on required precision, rounding rules, and performance constraints. It covers IEEE 754 binary representation, floating-point equality pitfalls, tolerance-based comparisons, and production concerns such as serialization, testing, and concurrency. ### Source excerpt There is an evergreen debate in the Java world: should you always use BigDecimal for money? The short answer is no. The real answer is: it depends on your computational context: the precision you need, the rounding rules you must follow, and the performance budget you have. The problem is that this conversation is often driven by dogma rather than engineering. ## 【eBPF 内核实现深度拆解】从验证器到 JIT，从 BTF 到调度器 DevFeed: [【eBPF 内核实现深度拆解】从验证器到 JIT，从 BTF 到调度器]() Original publisher: [Read original article]() Author: Liao Tonglang Published: 2026-06-12T00:00:00Z Content type: article Language: zh Sources: [土法炼钢 - 系统与基础设施]() Topics: [eBPF](), [Linux](), [JIT](), [Benchmark](), [clang](), [hash](), [benchmarking](), [RISC-V](), [ast-matchers]() Tags: [arm](), [array](), [benchmark](), [bpf-jit](), [bpf-maps](), [bpf-verifier](), [btf](), [clang](), [co-re](), [cpu](), [ebpf](), [fentry](), [hash](), [jit](), [kernel](), [libbpf](), [linux](), [linux-kernel](), [precision](), [risc-v](), [sched-ext](), [trampoline](), [x86](), [xdp]() ### AI overview This Chinese-language series systematically explains eBPF's Linux kernel implementation, covering the BPF instruction set and registers, verifier algorithms, JIT compilation, map data structures and concurrency, helper type checking, BTF and CO-RE relocation, libbpf loading, trampolines, and sched_ext interfaces. It is aimed at engineers who want to understand eBPF kernel source code and build production BPF programs. ### Source excerpt eBPF 内核虚拟机内部实现系统讲解：BPF 指令集与寄存器机器、验证器的抽象解释与状态裁剪、JIT 编译器后端、Map 各类型的并发与内存模型、helper 函数注册与类型检查、BTF 格式规范与 CO-RE 重定位引擎、libbpf 加载器工程、fentry/fexit 蹦床机制、sched_ext 调度器内核接口。面向想读懂 eBPF 内核源码、写生产级 BPF 程序的系统工程师。 ## Only 17% of all 64-bit Integers are products of two 32-bit integers DevFeed: [Only 17% of all 64-bit Integers are products of two 32-bit integers]() Original publisher: [Read original article]() Author: Daniel Lemire Published: 2026-05-22T01:16:35Z Content type: article Language: en Sources: [Daniel Lemire]() Topics: [Programming](), [hash]() Tags: [cryptographic](), [hash](), [numbers](), [precision](), [programming]() ### AI overview The article examines what fraction of 64-bit integers can be represented as the full product of two 32-bit integers. It presents the result that only 17% can be produced this way and relates the question to hash-function design and multiplication behavior. ### Source excerpt In software programming, the product between two integers is often computed to a fixed number of bits with overflow. Consider 8-bit integers. If you multiply 127 by 127, you get back the number 1 as an 8-bit unsigned integer, with an overflow. The actual full product is 16129. To represent 16129, you typically use 16 ... Continue reading Only 17% of all 64-bit Integers are products of two 32-bit integers ## Wastrel Compiles Hoot Scheme-to-WebAssembly Output DevFeed: [Wastrel Compiles Hoot Scheme-to-WebAssembly Output]() Original publisher: [Read original article]() Author: Andy Wingo Published: 2026-03-31T20:34:23Z Content type: article Language: en Sources: [wingolog]() Topics: [WebAssembly](), [Compiler](), [scheme]() Tags: [accidentally-quadratic](), [aot](), [bigint](), [bignums](), [compiler](), [compilers](), [exception-handling](), [exceptions](), [gc](), [gcc](), [gmp](), [hoot](), [igalia](), [library](), [maps](), [precision](), [scheme](), [standard](), [tail-calls](), [wasm](), [wastrel](), [webassembly](), [whippet]() ### AI overview The article describes recent work on Wastrel, an ahead-of-time WebAssembly compiler, including compiling output from the Hoot Scheme-to-Wasm compiler. It covers implementing bignum operations with mini-gmp and updating Hoot to use standardized WebAssembly exception handling. ### Source excerpt Good day! Today (tonight), some notes on the last couple months of Wastrel, my ahead-of-time WebAssembly compiler. Back in the beginning of February, I showed Wastrel running programs that use garbage collection, using an embedded copy of the Whippet collector, specialized to the types present in the Wasm program. But, the two synthetic GC-using programs I tested on were just ported microbenchmarks, and didn't reflect the output of any real toolchain. In this cycle I worked on compiling the output from the Hoot Scheme-to-Wasm compiler. There were some interesting challenges! bignums When I originally wrote the Hoot compiler, it targetted the browser, which already has a bignum implementation in the form of BigInt, which I worked on back in the day. Hoot-generated Wasm files use host bigints via externref (though wrapped in structs to allow for hashing and identity). In Wastrel, then, I implemented the imports that implement bignum operations: addition, multiplication, and so on. I did so using mini-gmp, a stripped-down implementation of the workhorse GNU multi-precision library. At some point if bignums become important, this gives me the option to link to the full GMP instead. Bignums were the first managed data type in Wastrel that wasn't defined as part of the Wasm module itself, instead hiding behind externref, so I had to add a facility to allocate type codes to these "host" data types. More types will come in time: weak maps, ephemerons, and so on. I think bignums would be a great proposal for the Wasm standard, similar to stringref ideally (sniff!), possibly in an attenuated form. exception handling Hoot used to emit a pre-standardization form of exception handling, and hadn't gotten around to updating to the newer version that was standardized last July. I updated Hoot to emit the newer kind of exceptions, as it was easier to implement them in Wastrel that way. Some of the problems Chris Fallin contended with in Wasmtime don't apply in the Wastrel case: sinc ## The CISO's Craft: Watchmaker or Gardener? DevFeed: [The CISO's Craft: Watchmaker or Gardener?]() Original publisher: [Read original article]() Author: Phil Venables Published: 2026-01-24T16:39:53Z Content type: opinion Language: en Sources: [Risk and Cyber]() Topics: [Security](), [Cybersecurity]() Tags: [ciso](), [craft](), [cybersecurity](), [leadership](), [precision](), [security]() ### AI overview The article considers whether security leaders should operate more like precise watchmakers, adaptive gardeners, or both when leading organizational transformations. It also argues that cybersecurity benchmarking should focus on control effectiveness and outcomes rather than inputs such as budgets. ### Source excerpt Some time ago I saw a comment about the distinction between acting like a "watchmaker" or a "gardener" when undertaking organization transformations. I misplaced the original reference so, unfortunately, I can't credit appropriately. But, I've been thinking a lot about what this would mean in the context of security leadership. Specifically, should the CISO be a watchmaker or a gardener, or both? The Watchmaker CISO: Precision and Control Imagine a master watchmaker, meticulously crafting... ## Comparing Integers and Doubles DevFeed: [Comparing Integers and Doubles]() Original publisher: [Read original article]() Author: Thomas Neumann (noreply@blogger.com) Published: 2025-11-10T16:55:00Z Content type: article Language: en Sources: [Database Architects]() Topics: [floating-point](), [SQL](), [DuckDB](), [sql-server](), [Programming](), [C++]() Tags: [c-plus-plus](), [compiler](), [duckdb](), [floating-point](), [precision](), [sql](), [sql-server](), [testing](), [undefined-behavior]() ### AI overview The article explains how comparing large integers with double-precision values can lose integer precision and produce non-transitive results in SQL systems. It describes how this can cause differences between ordinary comparisons and hash joins, and outlines a conversion-based approach for correct comparisons. ### Source excerpt During automated testing we stumbled upon a problem that boiled down to transitive comparisons: If a=b, and a=c, when we assumed that b=c. Unfortunately that is not always the case, at least not in all systems. Consider the following SQL query: select a=b, a=c, b=c from (values( 1234567890123456789.0::double precision, 1234567890123456788::bigint, 1234567890123456789::bigint)) s(a,b,c) If you execute that in Postgres (or DuckDB, or SQL Server, or ...) the answer is (true, true, false). That is, the comparison is not transitive! Why does that happen? When these systems compare a bigint and a double, they promote the bigint to double and then compare. But a double has only 52 bits of mantissa, which means it will lose precision when promoting large integers to double, producing false positives in the comparison. This behavior is highly undesirable, first because it confuses the optimizer, and second because (at least in our system) joins work very differently: Hash joins promote to the most restrictive type and discard all values that cannot be represented, as they will never produce a join partner for sure. For double/bigint joins that leads to observable differences between joins and plain comparisons, which is very bad. How should we compare correctly? Conceptually the situation is clear, an IEEE 754 floating point with sign s, mantissa m, and exponent e represents the values (-1)^s*m*2^e, we just have to compare the integer with that value. But there is no easy way to do that, if we do a int/double comparison in, e.g., C++, the compiler does the same promotion to double, messing up the comparison. We can get the logic right by doing two conversions: We first convert the int to double and compare that. If the values are not equal, the order is clear and we can use that. Otherwise, we convert the double back to an integer and check if the conversion rounded up or down, and handle the result. Plus some extra checks to avoid undefined behavior (the conversion of intma ## Floating-Point Units on Espressif SoCs: Why (and when) they matter DevFeed: [Floating-Point Units on Espressif SoCs: Why (and when) they matter]() Original publisher: [Read original article]() Author: John Lee Published: 2025-10-13T00:00:00Z Content type: article Language: en Sources: [Blog on Developer Portal]() Topics: [Espressif](), [Hardware](), [cpu](), [ESP32-S3](), [ESP32-C3](), [Benchmark](), [math]() Tags: [benchmark](), [blog](), [esp32](), [esp32-c3](), [esp32-s3](), [espressif](), [floating-point](), [fpu](), [hardware](), [math](), [performance](), [precision](), [processor]() ### AI overview This article explains what floating-point units are, why they matter for calculations involving decimal values and wide dynamic ranges, and how they differ across Espressif SoCs. It states that the ESP32-S3 performs floating-point operations directly in hardware, while the ESP32-C3 executes them in software, and introduces a benchmark-based discussion of performance. ### Source excerpt In this article, you'll learn what an FPU is, why it's useful, which Espressif SoCs feature one, and how it impacts performance through a benchmark. ## SOTA OCR with Core ML and dots.ocr DevFeed: [SOTA OCR with Core ML and dots.ocr]() Original publisher: [Read original article]() Author: Christopher Fleetwood; Pedro Cuenca Published: 2025-10-02T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [MLX](), [PyTorch]() Tags: [apple](), [architecture](), [battery](), [coreml](), [cpu](), [developers](), [framework](), [gpu](), [high-performance](), [images](), [mlx](), [model](), [ocr](), [on-device](), [parameter](), [precision](), [pytorch](), [repo](), [tools](), [vision]() ### AI overview This tutorial explains how to convert dots.ocr from PyTorch to Core ML for on-device execution on Apple hardware. It discusses the roles of the Neural Engine, GPU, MLX, and Core ML, then outlines a staged conversion process beginning with GPU execution, FLOAT32 precision, and static shapes. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## SwiNOG 40: Deploying Precision Time Protocol across WAN DevFeed: [SwiNOG 40: Deploying Precision Time Protocol across WAN]() Original publisher: [Read original article]() Published: 2025-09-23T05:33:00Z Content type: article Language: en Sources: [ipSpace.net blog]() Topics: [Network](), [Protocol (disambiguation)]() Tags: [infrastructure](), [network](), [precision](), [protocol](), [ptp](), [synchronization](), [time](), [wide](), [worth-reading]() ### AI overview The article discusses deploying Precision Time Protocol across a country-wide WAN to achieve nanosecond-level synchronization between cities. It states that this requires dedicated infrastructure and points to a SwiNOG 40 presentation by Oliver Ettlin for more details. ### Source excerpt Is it possible to deploy Precision Time Protocol across a country-wide WAN network and reach nanosecond-level synchronization between cities? It's definitely not trivial and only works over dedicated infrastructure; for more details, watch the PTP in WANs (video) presentation Oliver Ettlin had at SwiNOG 40. ## Falcon-Edge: A series of powerful, universal, fine-tunable 1.58bit language models. DevFeed: [Falcon-Edge: A series of powerful, universal, fine-tunable 1.58bit language models.]() Original publisher: [Read original article]() Author: Younes B; Qiyang Zhao; Hang Zou; Rhaiem; Ilyas Chahed; Maksim Velikanov; Jingwei Zuo; Mike Lubinets; Hakim Hacid; Falcon LLM TII UAE Published: 2025-05-15T13:13:45Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Large Language Model](), [LLM Techniques](), [quantization](), [Compression](), [Fine-tuning](), [Training AI Models](), [hugging face](), [AI Inference]() Tags: [compression](), [fine-tuning](), [floating-point](), [hugging-face](), [language-models](), [precision](), [quantization](), [train](), [training]() ### AI overview The article introduces Falcon-Edge, a series of 1.58-bit language models based on the BitNet architecture. The models use ternary weights during training to reduce memory use and improve deployment efficiency, and are offered in 1-billion- and 3-billion-parameter base and instruction-tuned variants. The article describes their training approach, fine-tuning variants, and evaluation on the former Hugging Face leaderboard v2 benchmark. ### Source excerpt A Blog post by Technology Innovation Institute on Hugging Face ## Introducing AutoRound: Intel's Advanced Quantization for LLMs and VLMs DevFeed: [Introducing AutoRound: Intel's Advanced Quantization for LLMs and VLMs]() Original publisher: [Read original article]() Author: wenhua cheng; Haihao Shen; weiweiz1; Heng Guo; Huang, Tai; Ke Ding; Ilyas Moutawwakil; Marc Sun; Mohamed Mekkouri Published: 2025-04-29T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [quantization](), [Large Language Model](), [LLMs](), [intel](), [GPU](), [hugging face](), [deepseek](), [llama](), [qwen]() Tags: [architectures](), [artificial-intelligence](), [cpu](), [cuda](), [deepseek](), [efficiency](), [gpu](), [hugging-face](), [inference](), [intel](), [language-models](), [llama](), [llms](), [model](), [offline](), [open-source](), [post-training](), [precision](), [quantization](), [qwen](), [vlms]() ### AI overview This article introduces AutoRound, Intel's weight-only post-training quantization method for LLMs and VLMs. It uses signed gradient descent to optimize weight rounding and clipping ranges, supports low-bit formats from INT2 to INT8, and aims to preserve accuracy with efficient quantization. The article describes its model, hardware, export-format, calibration, and tuning-recipe support, including performance on low-bit benchmarks. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Efficient Distributed Unique Timestamp Identifier Generation DevFeed: [Efficient Distributed Unique Timestamp Identifier Generation]() Original publisher: [Read original article]() Author: Peter Lawrey (noreply@blogger.com) Published: 2024-12-08T19:51:00Z Content type: tutorial Language: en Sources: [Vanilla Java]() Topics: [identifier](), [systems](), [Microservice](), [Concurrency](), [ordering](), [Latency]() Tags: [benchmarks](), [concurrent](), [distributed](), [identifier](), [latency](), [microservices](), [ordering](), [performance](), [precision](), [unique]() ### AI overview The article presents a distributed identifier scheme that embeds a host identifier into a nanosecond-resolution timestamp. It describes the resulting 64-bit identifiers as globally unique, human-readable, chronologically sortable, and suitable for high-concurrency, latency-sensitive systems. ### Source excerpt Distributed unique timestamp identifiers provide a powerful means of generating globally unique, human-readable 64-bit values at sub-microsecond speeds. By embedding a host identifier directly into a nanosecond-resolution timestamp, you gain a simple, chronologically sortable, and intuitive scheme for correlating events across multiple hosts. This approach offers significant benefits in latency-sensitive systems where even small delays can become expensive at scale. Introduction In a world of horizontally scaled microservices, ensuring that each event or message receives a unique identifier across multiple machines can be challenging. Traditional approaches often rely on UUIDs, which--while easy to use--lack intuitive readability and can be relatively expensive to generate in ultra-low-latency scenarios. Our solution builds upon nanosecond-resolution timestamps combined with a host identifier embedded directly into the lower-order digits of the timestamp. This technique, inspired by previous work on system-wide unique nanosecond timestamps, creates identifiers that are compact, human-interpretable, and extremely fast to produce. In essence, we treat time itself as the source of uniqueness. By carefully structuring the timestamp and assigning a unique hostId per machine (or per logical partition), we can scale to produce up to one billion unique 64-bit identifiers per second. These identifiers repeat only after centuries, making them suitable for long-running systems and distributed architectures that demand both precision and high performance. Concurrent identifier generation in a distributed system In distributed environments, colliding identifiers can lead to data corruption, misrouted requests, or difficulty in debugging. Although UUIDs solve uniqueness issues, they do not inherently convey temporal ordering or machine origin. More subtle forms of identifiers, such as database sequence numbers or custom counters, often need to be more convenient when synchronising ## Why Does Math.round(0.49999999999999994) Round to 1? DevFeed: [Why Does Math.round(0.49999999999999994) Round to 1?]() Original publisher: [Read original article]() Author: Peter Lawrey (noreply@blogger.com) Published: 2024-12-07T21:02:00Z Content type: tutorial Language: en Sources: [Vanilla Java]() Topics: [floating-point](), [Java](), [Programming](), [implementation]() Tags: [article](), [code](), [exercise](), [floating-point](), [info](), [java](), [precision](), [programming](), [puzzles]() ### AI overview This article explains why Java 6 can return 1 when Math.round() is applied to a value slightly below 0.5. It attributes the result to binary floating-point representation, rounding behavior, and implementation details, and contrasts Java 6 with Java 7. ### Source excerpt 1. Defining the Problem In many numerical computations, one would reasonably expect that rounding 0.499999999999999917 should yield 0, since it appears to be slightly less than 0.5. Yet, in Java 6, calling Math.round() on this value returns 1, a result that may initially seem baffling. This seemingly minor discrepancy stems from the interplay of binary floating-point representation, rounding modes, and the particular internal implementation details of Math.round() in earlier Java releases. For professionals in performance-sensitive environments--such as those working in financial technology or high-precision scientific applications--understanding these subtleties is more than just an academic exercise. Even tiny rounding differences can influence trading algorithms, pricing models, or simulations. Moreover, developers and enthusiasts who appreciate the low-level mechanics behind Java's numeric types will find valuable insights into how these internal workings affect everyday programming tasks. This article delves into why this unexpected rounding occurs, sheds light on the constraints of double-precision arithmetic, and contrasts the behaviour in Java 6 against newer versions like Java 7. Consider, for instance, the closely related question: Why does Math.round(0.49999999999999994) return 1 rather than 0? Although it might initially seem like a bug, it is, in fact, a predictable outcome once we acknowledge the inherent imprecision of floating-point arithmetic. By the end, you will have a clearer understanding of why these rounding anomalies happen, and how to avoid or mitigate their effects in your own code. 2. The IEEE 754 64-bit Double-Precision Format Component Bit Count Interpretation Sign 1 Determines the sign of the number: 0 indicates a positive value, 1 indicates a negative value. Exponent 11 Encodes the exponent using a bias of 1023. The stored value E is interpreted as E - 1023 for the actual exponent. Mantissa (Fraction) 52 Represents the significand (fract ## Accelerating Protein Language Model ProtST on Intel Gaudi 2 DevFeed: [Accelerating Protein Language Model ProtST on Intel Gaudi 2]() Original publisher: [Read original article]() Author: Julien Simon; Jiqing.Feng; Santiago Miret; Xinyu Yuan; Yi Wang; Matrix Yao; Minghao Xu; Ke Ding Published: 2024-07-03T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Large Language Model](), [Inference](), [Inference Performance](), [Fine-tuning](), [intel](), [Open Source](), [AI, ML & Data Engineering]() Tags: [accelerators](), [artificial-intelligence](), [batch](), [datasets](), [fine-tuning](), [hugging-face](), [inference](), [inference-performance](), [intel](), [language-models](), [llm](), [model](), [nvidia](), [optimum](), [partnerships](), [pcie](), [precision](), [transformers](), [zero-shot]() ### AI overview This tutorial explains how to run inference and fine-tune ProtST, a multimodal protein language model, using Intel Gaudi 2 accelerators and the Optimum for Intel Gaudi open-source library. It compares ProtST inference on NVIDIA A100 and Gaudi 2, reporting identical accuracy and 1.76x faster inference on Gaudi 2. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. [Next page]()