# tables Published articles for tables. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## StreamTab: Training-Free Temporal Link Prediction with Tabular Foundation Models DevFeed: [StreamTab: Training-Free Temporal Link Prediction with Tabular Foundation Models]() Original publisher: [Read original article]() Author: Mingqi Yang, Renqi Wu Published: 2026-11-09T05:00:00Z Content type: article Language: en Sources: [arXiv: Databases](), [arXiv: Machine Learning]() Topics: [Predictive AI](), [AI Foundation Models]() Tags: [cs-db](), [cs-lg](), [cs-si](), [datasets](), [foundation-models](), [graph](), [graph-neural-networks](), [history](), [interaction](), [link](), [points](), [precision](), [prediction](), [raises](), [random](), [sampling](), [statistics](), [synthetic](), [tables](), [temporal](), [training]() ### AI overview StreamTab predicts future links without training model parameters for each graph: it feeds a frozen tabular foundation model statistics of candidate pairs' past interactions alongside recent positive interactions and sampled negatives as labeled examples. Its regime conditioning chooses negatives to match the candidate sampling setup, tailoring the examples to the distinction required; as interactions arrive, it refreshes statistics and examples while keeping the model fixed. Across 13 datasets and 21 temporal graph baselines, it reports the highest average precision on 12 datasets under historical sampling and 11 under inductive sampling. Under random sampling, it ranks first or within one point of the best on seven datasets. Compared with random-negative examples using the same features and model, regime conditioning raises average precision by a median of 12.6 points under historical sampling. ### Source excerpt arXiv:2611.09052v1 Announce Type: new Abstract: Temporal link prediction uses past interactions to predict whether two nodes will interact in the future. Temporal graph neural networks achieve strong results on this task, but they are trained separately for each graph. How a model should use interaction history also depends on the candidates it is compared with. A past interaction can distinguish a recurring link from a random pair that has never interacted, but when the negative candidates have also interacted before, other signals are needed. We propose StreamTab, a training-free framework built on a frozen tabular foundation model pretrained only on synthetic tables. StreamTab describes each candidate pair by statistics of its past interactions and shows the model recent observed interactions and sampled negative pairs as labeled examples, from which the model scores new candidates. Its central design, regime conditioning, samples the negative examples according to how the candidates are sampled, so the examples show the distinction the model must make. As new interactions arrive, StreamTab updates the statistics and refreshes the examples while the model parameters stay fixed. On thirteen datasets against 21 temporal graph baselines, StreamTab achieves the highest average precision on twelve datasets under historical sampling and eleven under inductive sampling, and ranks first or stays within one point of the best on seven datasets under random sampling. With the same features and model, regime conditioning raises average precision over examples with random negatives by a median of 12.6 points under historical sampling. ## Oracle to S3 as Parquet: Export Tables and Replicate Changes with CDC DevFeed: [Oracle to S3 as Parquet: Export Tables and Replicate Changes with CDC]() Original publisher: [Read original article]() Author: Seán Whelan Published: 2026-11-06T12:27:12Z Content type: article Language: en Sources: [Estuary]() Topics: [streaming data to S3 with Parquet](), [parquet](), [Parquet files in Amazon S3](), [Oracle Database](), [AWS Glue](), [Debezium Connector]() Tags: [aws](), [aws-glue](), [cdc](), [changes](), [compare](), [dms](), [export](), [goldengate](), [managed](), [oracle](), [parquet](), [pipeline](), [replicate](), [s3](), [tables]() ### AI overview Oracle-to-S3 Parquet choices depend on whether you need change capture: DMS and log-based CDC tools read redo and capture deletes, while exports and scheduled query jobs see surviving rows only. All approaches must handle Oracle type mapping and immutable Parquet files, where updates and deletes become new rows; current-state queries therefore deduplicate by key and filter the latest delete. The excerpt details DMS setup, including archived redo and supplemental logging, Parquet and timestamp settings, bounded LOB sizing, and file batching. It also describes Estuary's LogMiner capture and S3 materialization: backfill runs alongside redo mining, changes append on a configurable upload interval, and deletes and key changes are represented as rows. Estuary's stated tradeoffs include skipped LOB and several other column types, string or numeric mappings that may need configuration, no hard-delete or truncate handling, and retained historical files. Alternatives include Autonomous Database exports for simple full-query exports, and Spark or Glue for custom transforms; those query-based approaches do not capture deletes. ### Source excerpt Move Oracle to S3 as Parquet with AWS DMS, AWS Glue, GoldenGate or a managed CDC pipeline. Compare how each handles NUMBER, DATE, LOBs and deletes. ## Unraveling Groundwater Stress: A Model Generalizable Across Space and Time Reveals Co-Declining Water Tables and Recharge Across Bangladesh DevFeed: [Unraveling Groundwater Stress: A Model Generalizable Across Space and Time Reveals Co-Declining Water Tables and Recharge Across Bangladesh]() Original publisher: [Read original article]() Author: Saleh Sakib Ahmed, Rashed Uz Zzaman, Saifur Rahman Jony, Faizur Rahman Himel, Afroza Sharmin, A. H. M. Khalequr Rahman, M. Sohel Rahman, Sara Nowreen Published: 2026-11-06T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence](), [arXiv: Machine Learning]() Topics: [Machine learning](), [machine learning overfitting]() Tags: [analysis](), [bangladesh](), [cs-ai](), [cs-lg](), [data](), [develop](), [enhancement](), [framework](), [generate](), [learning](), [location](), [machine](), [machine-learning](), [maps](), [measurements](), [model](), [monitoring](), [space](), [spatial](), [tables](), [time](), [water]() ### AI overview The Teacher-Student framework reconstructs Bangladesh groundwater depth at 2-km resolution from sparse annual in-situ observations and 25-km GLDAS storage data. The Student achieves R²=0.93 for 2003-2022 and on held-out years, and R²=0.70 on held-out districts, allowing reconstruction without contemporary in-situ measurements. It finds widespread water-table deepening and a national recharge decline of 1.10 mm/yr (p<0.001). Autocorrelation-corrected results show significant recharge declines across 55.6% of the country versus increases across 4.9%, suggesting pumping-related recharge enhancement remains localized. ### Source excerpt arXiv:2503.22771v2 Announce Type: replace-cross Abstract: Is Bangladesh replenishing the groundwater it depends on? Answering this question remains difficult in Bangladesh and worldwide because sparse in-situ measurements limit observational capabilities, while existing machine-learning approaches struggle to generalize across time and space. To address this bottleneck, we develop a Teacher-Student framework in which a Teacher accurately learns from sparse annual in-situ minimum (R2 = 0.86) and maximum (R2 = 0.96) depth observations to generate dense spatial patterns, and a Student transfers these patterns from time-varying 25-km GLDAS groundwater storage to high-resolution (2-km) depth maps using location-specific features. The Student achieves R2 = 0.93 over 2003-2022 and generalizes across unseen years (Leave-One-Year-Out R2 = 0.93) and locations (Leave-One-District-Out R2 = 0.70), enabling reconstruction without contemporary in-situ measurements. The reconstruction reveals widespread water-table deepening and a national decline in groundwater recharge of 1.10 mm/yr (p < 0.001). Autocorrelation-corrected analysis identifies statistically significant recharge declines across 55.6% of Bangladesh, compared with significant increases across only 4.9%. This imbalance suggests that pumping-induced recharge enhancement, as proposed by the Bengal Water Machine hypothesis, remains localized rather than widespread across Bangladesh. Together, these findings reveal widespread weakening of groundwater replenishment and provide a scalable framework for monitoring groundwater dynamics in data-sparse aquifers globally. ## Which Data Collaboration Tool: OpenSharing, Clean Rooms, Marketplace DevFeed: [Which Data Collaboration Tool: OpenSharing, Clean Rooms, Marketplace]() Original publisher: [Read original article]() Author: Jordan Soldo Published: 2026-11-05T16:29:00Z Content type: comparison Language: en Sources: [Databricks]() Topics: [Tool]() Tags: [agents](), [ai](), [ai-agents](), [ai-models](), [ai-products](), [apps](), [asset](), [assets](), [best-practices](), [bi](), [bi-tools](), [code](), [credentials](), [data](), [data-strategy](), [databricks](), [dataset](), [marketplace](), [share](), [sharing](), [tables](), [tool]() ### AI overview Choose based on the relationship and access you want: OpenSharing gives a known recipient direct, read-only access to live assets, including tables, views, volumes, notebooks, UDFs and AI models, avoiding separate data copies and delivery pipelines. Clean Rooms suit a known partner when both sides want to combine proprietary data without exposing full datasets; participants approve the code, and see its results rather than each other's underlying data. Marketplace is a public, discoverable storefront for data and AI products when providers want to reach an unspecified audience or consumers want to find offerings. The options can be used for different relationships by the same business. ### Source excerpt The world of sharing has extended far past tables. We can now securely share all... ## Perceptive Humanoid Motion from Simulation DevFeed: [Perceptive Humanoid Motion from Simulation]() Original publisher: [Read original article]() Author: Siyuan Hu, Abulikemu Abuduweili, Yue Meng, Shipeng Liu, Quentin Leboutet, Chen Chen, Matthias M\\"uller, Vladlen Koltun Published: 2026-11-05T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Humanoid Robots](), [Physical AI](), [Robot Manipulation]() Tags: [autonomy](), [compute](), [cs-ai](), [cs-cv](), [cs-lg](), [cs-ro](), [data](), [environments](), [halo](), [home](), [homes](), [horizon](), [humanoid](), [humanoid-robots](), [labor](), [language](), [low-level](), [mapping](), [pipeline](), [simulation](), [skills](), [solve](), [system](), [tables]() ### AI overview HALO addresses the shortage of paired vision, language, and joint-command training data by synthesizing vision-language-motion examples across 1,000 procedurally generated homes, scaling data production with compute rather than human labor. The system learns skills such as navigating to named objects, moving through doors, crouching or crawling under tables, and pushing boxes, then combines them for long-horizon tasks. Though trained without real homes, HALO reportedly transferred zero-shot to six unmapped real-world environments, with 91% success across 360 trials. ### Source excerpt arXiv:2611.06908v1 Announce Type: cross Abstract: Humanoid robots have the potential to automate a wide range of physical tasks. Progress toward generalist humanoid autonomy, however, remains constrained by a data bottleneck: mapping pixels and free-form language to low-level joint commands requires paired supervision, and no corpus of such data exists at sufficiently large scale. Some approaches rely on human teleoperation and wearable capture devices, which require at least an hour of human time to produce an hour of data. We present HALO, a humanoid autonomy system powered by a pipeline that synthesizes vision-language-motion data across a thousand procedurally generated homes. The corpus scales with compute rather than human labor. HALO acquires a repertoire of physical skills, including sitting on chairs, navigating to named objects, stepping sideways through half-open doors, crouching and crawling beneath tables, and pushing boxes. It orchestrates these skills to solve composite, long-horizon tasks. Without ever seeing a real home during training, HALO transfers zero-shot to the physical world, achieving a 91% success rate across 360 trials in six unmapped real-world environments. See https://apple-aiml-research.github.io/ml-halo/ for code and videos. ## TabPFN-3.5: Technical Report DevFeed: [TabPFN-3.5: Technical Report]() Original publisher: [Read original article]() Author: Benjamin J\\"ager, Nick Erickson, L\\'eo Grinsztajn, Felix Birkel, Klemens Fl\\"oge, Oscar Key, K\\"ur\\c{s}at Kaya, Jonas K\\"ubler, Ad\\\`ele Frankel, Tobias Schr\\"oder, Anurag Garg, Jan Hendrik Metzen, Dav Published: 2026-11-04T05:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [AI Foundation Models](), [Machine learning](), [Time Series]() Tags: [abstract](), [advanced](), [arxiv](), [cardinality](), [cs-lg](), [features](), [forecasting](), [foundation](), [foundation-model](), [images](), [machine-learning](), [multimodal](), [prediction](), [problems](), [stat-ml](), [strings](), [tables](), [technical](), [temporal](), [text](), [time-series](), [wide]() ### AI overview TabPFN-3.5 reports broader tabular prediction performance than TabPFN-3 and existing baselines, including state-of-the-art results on TabArena and relational data, with stronger time-series forecasting. It extends coverage to temporal or grouped splits, tables containing strings, text, or images, high-cardinality categories, and wide tables. TabPFN-3.5-Fast runs up to 3x faster than TabPFN-3 while retaining most accuracy gains. The Plus variant adds advanced text and date handling and proprietary inference optimizations; Thinking scales inference-time computation, with improvements reported to make it up to 12x faster than TabPFN-3-Thinking. These are abstract-level claims; details and evaluation evidence are not included in the supplied excerpt. ### Source excerpt arXiv:2609.17895v3 Announce Type: replace Abstract: We introduce TabPFN-3.5, our new flagship Tabular Foundation Model. It significantly outperforms its predecessor, TabPFN-3, and all existing baselines across a broad range of tabular problems. TabPFN-3.5 sets a new state of the art on standard tabular prediction in TabArena, and extends it to the data practitioners encounter in practice: non-i.i.d. data with temporal or grouped splits, tables with strings, text and images, high-cardinality categorical features, and wide tables with many features. These gains carry over to our task-specific harnesses: state of the art on relational data and stronger time-series forecasting. For faster inference, our variant TabPFN-3.5-Fast runs up to 3x faster than TabPFN-3 while keeping most of the accuracy gains. In addition, we upgrade TabPFN-3.5-Plus, expanding our multimodal capabilities with advanced text and date handling alongside proprietary inference optimizations. Finally, we release a new version of our Thinking mode, TabPFN-3.5-Thinking, which scales inference-time computation to push the state of the art further. It benefits from our stronger base model and from inference-time improvements that make it up to 12x faster than TabPFN-3-Thinking. ## Towards Error-Free EHRs: Reasoning-Intensive Consistency Verification Between Clinical Notes and Structured Tables in Electronic Health Records DevFeed: [Towards Error-Free EHRs: Reasoning-Intensive Consistency Verification Between Clinical Notes and Structured Tables in Electronic Health Records]() Original publisher: [Read original article]() Author: Yeonsu Kwon, Jiho Kim, Junseong Choi, Paloma Rabaey, Minseo Kim, Sujeong Im, Jeewon Yang, Jun-Min Lee, Sangji Lee, Jiwon Kim, Hangyul Yoon, Hyunwook Kwon, Edward Choi Published: 2026-11-04T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Data Quality](), [Artificial Intelligence](), [llm-reasoning](), [Ground truth / benchmark quality]() Tags: [benchmark](), [consistency](), [cs-ai](), [cs-cl](), [data](), [dataset](), [decision-making](), [documentation](), [electronic-health-records](), [event](), [events](), [framework](), [llm](), [matching](), [model](), [note](), [notes](), [performance](), [reasoning](), [structured](), [tables](), [verification]() ### AI overview EHR-ReasonCon is a benchmark for checking whether structured EHR tables agree with clinical notes when verification requires clinical interpretation, event relationships, and temporal reasoning. Built from MIMIC-III with expert-guided annotations, it contains 8,042 entities. The accompanying EHR-INSPECTOR framework segments notes, extracts anchor entities and temporal references, then uses table-exploration tools to verify consistency. On expert-validated LLM-as-a-judge evaluations using harsh and lenient criteria, it reports state-of-the-art performance across multiple model backbones. The abstract also notes component analyses and differences between model and human verification; it provides no further details on those findings. ### Source excerpt arXiv:2605.26463v2 Announce Type: replace-cross Abstract: Data consistency between unstructured clinical notes and structured tables in Electronic Health Records (EHRs) is essential for patient safety and clinical decision-making. However, existing work on note-table consistency verification mainly relies on surface-level matching of numeric values or simple events. Such approaches fail to capture the reasoning underlying real-world EHR documentation, including clinical interpretation, event relations, and temporal changes. To address this gap, we introduce EHR-ReasonCon, a reasoning-intensive benchmark for note-table consistency verification. Built on MIMIC-III with expert-guided annotations, it comprises 8,042 entities derived from clinical notes and provides high-quality ground-truth labels. The annotation protocol is supported by specialized table-exploration tools to ensure systematic evidence retrieval and reliable consistency assessment. We also propose EHR-INSPECTOR, an LLM-based framework that segments notes, extracts anchor entities and temporal references, and uses table-exploration tools to verify consistency against structured tables. Evaluated using expert-validated LLM-as-a-judge metrics under harsh and lenient criteria, EHR-INSPECTOR achieves state-of-the-art performance across multiple model backbones. Analyses further demonstrate the effectiveness of its components and highlight differences from human verification. The code and dataset are available at https://github.com/dustn1259/EHR-ReasonCon. ## Speculative Ad-hoc Querying DevFeed: [Speculative Ad-hoc Querying]() Original publisher: [Read original article]() Author: Haoyu Li, Srikanth Kandula, Maria Angels de Luis Balaguer, Aditya Akella, Venkat Arun Published: 2026-11-03T05:00:00Z Content type: article Language: en Sources: [arXiv: Databases]() Topics: [Tool](), [Large Language Model](), [llm-reasoning](), [AI Agent]() Tags: [agent-workflows](), [agentic-systems](), [api](), [compilation](), [cs-ai](), [cs-db](), [cs-hc](), [cs-lg](), [cs-ma](), [database](), [i-o](), [language-models](), [large-language-models](), [large-language-models-llms](), [latency](), [llm](), [llms](), [productivity](), [queries](), [query](), [querying](), [reasoning](), [scheduler](), [sql](), [tables]() ### AI overview Wonder uses a small auxiliary LLM to prepare analytical SQL work while a primary agent reasons. Rather than predict an exact next query, it anticipates shared query shapes and data needs, materializes reusable superset results, then rewrites later queries to use them. Its scheduler gives foreground queries priority and uses reasoning intervals for speculation; people writing SQL can also guide preparation with incomplete queries and see intermediate results. In replayed agent workflows, Wonder reduced SQL execution time by up to 31.8% and combined reasoning and active SQL time by up to 22.6%, alongside about 6% higher recorded API cost and 10% more read I/O on the full dataset. A separate 24-person study reported lower latency on both analytical tasks, faster answers on one, and increased productivity for 87.5% of participants. ### Source excerpt arXiv:2503.00714v2 Announce Type: replace Abstract: Large language models (LLMs) invite substantial effort and investment, yet agentic systems can spend much of their time waiting for tools. This paper explores how a small auxiliary LLM can reduce these waits by speculatively preparing a database while the primary LLM reasons. The challenge is that the auxiliary model must anticipate the primary model's actions before the primary model takes them. Our key insight is that database systems give the speculator a structural advantage: it need not predict the exact next query. Instead, it can prepare work shared by many plausible queries. Predicting query shapes enables advance compilation, predicting data access warms caches, and predicting broader data requirements enables precomputation of superset results that subsequent queries can refine. We present Wonder, a system that exploits this advantage for analytical SQL workloads. Wonder constructs reusable superset queries, materializes their components, and rewrites subsequent queries against the resulting temporary tables. Its scheduler prioritizes foreground queries while using reasoning intervals for speculative preparation. The same engine supports humans writing SQL, using incomplete queries to guide speculation and displaying intermediate results as they type. In a replay-based evaluation of agent workflows, Wonder reduces SQL execution time by up to 31.8% and combined primary-agent reasoning and active SQL time by up to 22.6%, with approximately 6% higher recorded main-agent and prediction API cost and 10% more read I/O on the full dataset. A separate 24-participant study finds significantly lower query latency on both analytical tasks and improved time-to-answer on one, with 87.5% of participants reporting increased productivity. ## JetStream: Generating Verified Query Accelerators for Existing Database Systems DevFeed: [JetStream: Generating Verified Query Accelerators for Existing Database Systems]() Original publisher: [Read original article]() Author: Akhilesh Balasingam, Amadou Ngom, Geoffrey X. Yu, Tim Kraska Published: 2026-11-03T05:00:00Z Content type: article Language: en Sources: [arXiv: Databases]() Topics: [database-optimization](), [Database](), [SQL](), [DuckDB]() Tags: [accelerators](), [agentic-workflow](), [cs-db](), [database](), [differential-testing](), [duckdb](), [jetstream](), [other](), [performance](), [queries](), [query](), [recovery](), [specification](), [sql](), [state](), [static](), [storage](), [stream](), [substrate](), [systems](), [tables]() ### AI overview JetStream generates query-specific accelerators that extend an existing DBMS, operating on its native tables while the DBMS retains responsibility for storage, transactions, recovery, and other queries. A fixed, engine-neutral substrate handles engine-specific hooks and keeps auxiliary state transactionally consistent as data changes. Tested accelerators may use any optimization and are checked by differential testing; verified accelerators trade some performance for a machine-checked proof that answers satisfy a trusted specification derived from SQL. On TPC-H at SF=20, the abstract reports a 315x geometric-mean read speedup over DuckDB, or 93x for verified accelerators, and 120x speedup under a continuous stream of 10,000 modified rows per second. ### Source excerpt arXiv:2610.20527v2 Announce Type: replace Abstract: Recent work has shown that LLMs can synthesize highly specialized database systems for fixed workloads, but these systems replace the DBMS's native storage and assume static data: users must migrate their data, often give up ad-hoc queries, and lose acceleration once the data changes. We present JetStream, which instead generates query-specific accelerators that extend an existing DBMS. Given a set of parametrized query templates, a staged agentic workflow synthesizes accelerators that run directly over the DBMS's native tables and may maintain custom auxiliary state, while the DBMS remains responsible for storage, transactions, recovery, and all other queries. A fixed, engine-neutral substrate handles engine-specific hooks and keeps auxiliary state transactionally consistent under writes. JetStream generates two kinds of accelerators: tested accelerators, which may use any optimization and are validated by differential testing, and verified accelerators which trade some performance for a machine-checked proof that every answer satisfies a trusted specification derived from the SQL. On TPC-H at SF=20, JetStream achieves a $315 \times$ geomean read speedup over DuckDB ($93 \times$ verified) and retains $120 \times$ under a continuous update stream of 10,000 modified rows per second. ## FloppoScope is a browser-based floppy disk image analyzer DevFeed: [FloppoScope is a browser-based floppy disk image analyzer]() Original publisher: [Read original article]() Author: Anne Barela Published: 2026-11-02T20:27:32Z Content type: news Language: en Sources: [Adafruit Industries]() Topics: [Disk image](), [Web app]() Tags: [amiga](), [analyzer](), [boot](), [browser](), [c64](), [directory](), [disk-image](), [emulation](), [fat](), [filesystems](), [floppy](), [floppy-disk](), [flux](), [github](), [html](), [imd](), [mac](), [raw](), [tables](), [vintage](), [vintage-computing](), [web](), [web-browser]() ### AI overview FloppoScope is a single-file browser tool for inspecting floppy disk images without mounting or modifying them; files remain on the local machine. It reads raw sector images and KryoFlux or SCP flux captures, and can display boot records, allocation tables, directory trees and raw sectors across FAT, ProDOS, Amiga, C64, Mac and other filesystems. It can find deleted files and export to IMG, IMD or HFE. The unmapped-data scan is limited to FAT sector images, and decoding may be partial depending on the image. Run it from the project page or open the downloaded index.html in a modern browser, with no server, installation or build step. ### Source excerpt Developer 0ldStaph has posted FloppoScope on GitHub, a floppy disk image analyzer that runs in a web browser. Open a single image or a whole folder and look at the boot record, allocation tables, directory tree and raw sectors. Nothing gets mounted, and the images aren't modified. It reads KryoFlux and SCP flux captures along [...] ## Striped Backgrounds in CSS (Horizontal, Vertical, Diagonal) DevFeed: [Striped Backgrounds in CSS (Horizontal, Vertical, Diagonal)]() Original publisher: [Read original article]() Author: Marko Published: 2026-10-31T09:00:00Z Content type: tutorial Language: en Sources: [Marko Denic Blog]() Topics: [CSS](), [web animation](), [Media Queries]() Tags: [animation](), [background-size](), [css](), [css-gradients](), [examples](), [progress](), [tables](), [tutorial](), [vertical](), [web-development]() ### AI overview CSS stripes use hard color stops in gradients to create sharp edges, with `repeating-linear-gradient()` as the main method. The default direction creates horizontal stripes; `90deg` makes them vertical, and other angles create diagonal stripes. Set stop positions to adjust stripe widths. A tiled `linear-gradient()` with `background-size` also works for horizontal and vertical stripes and makes spacing easy to change, while repeating gradients produce seamless diagonals. The excerpt also shows layering translucent stripes over colors or images, using `:nth-child(even)` for zebra table rows, and animating a striped progress bar by one stripe period; it disables that animation for users who prefer reduced motion. Angled hard edges may appear jagged, and the article suggests a roughly one-pixel blend between colors to smooth them. ### Source excerpt Create striped backgrounds with pure CSS gradients: horizontal, vertical and diagonal stripes, pinstripes, zebra-striped tables and an animated striped progress bar. Copy-paste examples. ## Patch Commodore 64 Kernal Roms to remap keyboard DevFeed: [Patch Commodore 64 Kernal Roms to remap keyboard]() Original publisher: [Read original article]() Author: Anne Barela Published: 2026-10-30T14:58:52Z Content type: article Language: en Sources: [Adafruit Industries]() Topics: [keyboard]() Tags: [commodore-64](), [eeprom](), [emulator](), [keyboard](), [maps](), [patch](), [ram](), [reverse-engineering](), [shift](), [symbols](), [tables](), [vintage](), [vintage-computing]() ### AI overview 64KEY ROM PATCH remaps the Commodore 64 keyboard by finding the four key maps in KERNAL ROM--Normal, Shift, Commodore, and Control--and applying entries from a DATA list containing a shift mask, key scan code, and PETSCII character. It builds and activates a patched ROM in RAM, avoiding the earlier approach of loading a driver at a fixed address in BASIC RAM. The patched image can be saved and burned to EEPROM for a physical C64, selected through an emulator, or switched on compatible ROM-switching setups. The excerpt gives examples of symbol remappings but does not describe the full patch list or implementation. ### Source excerpt Dave VW has posted a new way to remap the Commodore 64 keyboard, writing at Tech with Dave. Earlier remapping drivers loaded into lower BASIC RAM at a fixed address and vanished on restart. The keyboard tables actually live in the KERNAL ROM, which holds four maps: Normal, Shift, Commodore, and Control. The new program, [...] ## TableTune: Measured, Self-Correcting Draft-Length Tables for Dynamic Speculative Decoding DevFeed: [TableTune: Measured, Self-Correcting Draft-Length Tables for Dynamic Speculative Decoding]() Original publisher: [Read original article]() Author: Abhishek Malvankar, Paul Castro, Nick Mitchell, Lionel Villard Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [LLM optimization](), [vllm](), [Large Language Model]() Tags: [controller](), [cs-ai](), [cs-et](), [decoding](), [dflash](), [gpu](), [gpus](), [llm-d](), [llm-serving](), [mixture-of-experts](), [qwen3](), [speculative-decoding](), [tables](), [throughput](), [vllm]() ### AI overview TableTune measures decoding time across batch sizes and draft lengths on vLLM's serving path, then builds a throughput-maximizing draft-length table; its vLLM plugin tracks accepted guesses in live traffic and rebuilds the table as conditions change. On an H100, the tuned table reached at least 94% of the best fixed-length throughput across loads in four of five drafter settings, and 88% in the fifth. Against same-engine TurboSpec- and Nightjar-style controls, its worst-case loss was no higher; with EAGLE-3, the loss was 3% versus 9% and 12%. On a mixture-of-experts model, it lost at most 3% versus up to 18% for vLLM's documented table. The controller improved throughput from mistuned tables by up to 58% (45% on vLLM's default runner) and recovered 96% of stale-table loss after a workload switch with DFlash. In the reported llm-d deployment, it was up to 32% faster than fixed draft length 3 and up to 25% faster than vLLM's documented table at high load. ### Source excerpt arXiv:2610.38968v1 Announce Type: cross Abstract: Speculative decoding speeds up large language model (LLM) serving only while the GPU has spare compute, so the best draft length, the number of tokens a small drafter guesses per step, shrinks as the batch grows. vLLM lets operators set the draft length for each batch size with a hand-written table, but nothing measures or corrects that table. We present TableTune. Its tuner times the decoding step for every batch size and draft length on vLLM's own serving path and computes, in minutes, the table that maximizes throughput. Its controller, a vLLM plugin, tracks how many guesses are accepted in live traffic and rebuilds the table while serving. We measure TableTune only against other ways to speculate. On an H100, the tuned table delivers at least 94% of the throughput of the best fixed draft length at every load in four of five drafter settings (88% in the fifth) without the operator having to know which length that is. Against reimplementations of TurboSpec- and Nightjar-style control on the same engine, its worst-case loss is never higher than theirs, and with the EAGLE-3 drafter it is 3% against 9% and 12%. On a mixture-of-experts model, vLLM's documented table loses up to 18% and the tuned table at most 3%. Starting from mistuned tables, the controller raises throughput by up to 58% (up to 45% on vLLM's default runner), and after a workload switch with the DFlash drafter it recovers 96% of a stale table's loss. In llm-d's optimized-baseline deployment as shipped, two Qwen3-32B replicas on four GPUs, the tuned table is up to 32% faster than a fixed draft length of 3 and up to 25% faster than vLLM's documented table at high load, and the controller raises the documented table's throughput by up to 29%. ## Under the Hood: How Room 3.0 and SQLDelight 2.x Detect Database Changes DevFeed: [Under the Hood: How Room 3.0 and SQLDelight 2.x Detect Database Changes]() Original publisher: [Read original article]() Author: chanzmao Published: 2026-10-29T17:35:32Z Content type: comparison Language: en Sources: [ProAndroidDev]() Topics: [sqldelight](), [Query (disambiguation)]() Tags: [android-app-development](), [android-development](), [changes](), [database](), [flow](), [kotlin-coroutines](), [mobile-app-development](), [notifications](), [query](), [room](), [software-architecture](), [sqldelight](), [tables]() ### AI overview Room 3.0 observes table invalidations: its Flow runs the query when collection starts, then reruns it after a relevant table change. This can rerun a query even when its result is unchanged; cancellation stops the observation. SQLDelight 2.x attaches a listener to a Query. `Query.asFlow()` emits the query as a notification, while downstream operators such as `mapToList()` execute SQL and fetch rows; listener cleanup occurs when collection closes or is cancelled. Neither mechanism avoids reading from the database after a relevant notification. The text cautions that SQLDelight's query-level listener does not guarantee fewer executions or prove that only affected queries are notified; behavior depends on notification and driver implementation details. ### Source excerpt Room and SQLDelight both let us observe database queries as Kotlin Flows. When the underlying data changes, the Flow emits an update, allowing the UI to react without manually refreshing the data. Image created by AI The result looks similar from the outside, but the two libraries handle change notifications differently. Room relies on InvalidationTracker to monitor database tables. SQLDelight uses listeners attached to individual Query objects. Let's look at how these mechanisms work internally and what the differences mean in practice. How Room 3.0 Monitors Database Changes Room uses InvalidationTracker to keep track of changes to database tables. When a DAO query returns a Flow, Room uses this mechanism to determine when the query should run again. The important detail is that Room tracks which tables a query depends on, rather than tracking changes to the query result itself. What happens behind the scenes? The following example illustrates the basic idea. It is simplified pseudocode, not Room's actual implementation. fun createRoomQueryFlow( tracker: InvalidationTracker, tables: Array, queryBlock: suspend () -> T ): Flow = flow { emit(queryBlock()) tracker.createFlow(*tables).collect { emit(queryBlock()) } } There are two things worth noting here. The query runs before change monitoring begins. When collection starts, queryBlock() fetches the current data and emits the result. The Flow then observes invalidation events from InvalidationTracker. A table change can trigger another query execution. Suppose a query reads from the users table. When Room detects a relevant change to that table, the Flow can emit another result by executing the query again. Room does not need to compare the old and new query results. It uses table invalidation as a signal that the result may have changed. This approach keeps the mechanism straightforward, but it has a trade-off: a change to a table can trigger re-execution even when the actual result of a particular que ## Turns out database backups are NOT trivial DevFeed: [Turns out database backups are NOT trivial]() Original publisher: [Read original article]() Author: Stack Published: 2026-10-29T15:30:13Z Content type: article Language: en Sources: [Stack]() Topics: [backups](), [Database](), [database-consistency](), [transaction-log](), [Transactions](), [DuckDB](), [clickhouse]() Tags: [abstraction](), [agent](), [algorithm](), [analytics](), [api](), [backing-up](), [backup](), [backups](), [convex](), [data](), [database](), [production](), [tables]() ### AI overview A row-by-row backup of a changing database can combine records from different moments and describe a state that never existed. Convex's data sync avoids this by alternating between reading table rows and catching up on the document log, keeping its snapshot within the retention window; it reports `upToDate` when the copy is consistent. Pages may contain repeated rows and arrive out of timestamp order, so callers must apply truncates and values and save pages safely. The endpoint requires a Pro plan and charges for database reads and bandwidth; its cursor can resume syncing within the deployment's retention window. Convex also offers dashboard and CLI backups, scheduled backups on Pro, and faster, cheaper physical backups for dedicated deployments, though those cannot be downloaded or inspected. For analytics at scale, the article says Convex still recommends its Fivetran connector. ### Source excerpt I thought backing up a database was simple, you just loop over the tables and copy the rows out. Nipunn sat me down at a whiteboard and showed me why that can give you a backup of data that never existed, and how Convex gets around it. ## The Frankenstein Table DevFeed: [The Frankenstein Table]() Original publisher: [Read original article]() Author: Preethi Published: 2026-10-29T13:47:08Z Content type: article Language: en Sources: [CSS-Tricks]() Topics: [CSS](), [layout](), [Front end]() Tags: [articles](), [assistive-technology](), [block](), [browser](), [browsers](), [contents](), [css](), [css-grid](), [display](), [flexbox](), [gap](), [grid](), [html](), [html-elements](), [keyboard](), [layout](), [layouts](), [markup](), [navigation](), [table](), [tables]() ### AI overview The "Frankenstein Table" uses table markup as a readable pseudo-semantic skin while CSS Grid controls layout. In the example, the table becomes a grid, `tbody` uses `display: contents`, and rows and their cells use `display: block` to bypass native table layout. The author says VoiceOver and keyboard navigation still work as with a real table in the browsers considered, while acknowledging the approach looks awkward and raises questions about separating content from presentation. The excerpt also stresses that ordinary HTML tables remain useful for semantic tabular data and assistive technology; Grid is valuable for responsive layout, source-order independence, and positioning. It presents this technique as a layout experiment, not a general replacement for tables. ### Source excerpt A super scary way to go about tables using other layout techniques, like CSS Grid. The Frankenstein Table originally handwritten and published with love on CSS-Tricks. You should really get the newsletter as well. ## Adaptive Integer Compression in TimescaleDB: Built for the Read Path DevFeed: [Adaptive Integer Compression in TimescaleDB: Built for the Read Path]() Original publisher: [Read original article]() Author: David Beck Published: 2026-10-29T12:02:58Z Content type: article Language: en Sources: [Timescale]() Topics: [Compression](), [Encoding]() Tags: [adaptive](), [compression](), [encoding](), [fast](), [foreign-keys](), [integer-compression](), [query](), [small](), [sources](), [status](), [tables](), [time-series](), [timescaledb]() ### AI overview Adaptive Integer Compression (AIC) selects an encoding for each block from options that decompress quickly. This addresses tables where integers include status codes, foreign keys, or interleaved sources, often arriving in small batches--data that DeltaDelta may not suit as well as time-series-shaped integers. The excerpt says queries reading this data must decompress it first; it does not provide performance measurements or describe the available encodings. ### Source excerpt DeltaDelta suits time-series-shaped integers. Real tables also hold status codes, foreign keys and interleaved sources, often in small batches. Every query that reads that data has to decompress it first, so AIC picks the encoding per block, and only from encodings that decompress fast. ## OrioleDB public beta: a Postgres storage engine for update-heavy tables DevFeed: [OrioleDB public beta: a Postgres storage engine for update-heavy tables]() Original publisher: [Read original article]() Author: Aditya Maruvada; Daniel Mitterdorfer; Alexander Korotkov; Artur Zakirov Published: 2026-10-28T07:00:00Z Content type: release Language: en Sources: [Supabase]() Topics: [Supabase](), [rdbms](), [RDS for PostgreSQL]() Tags: [beta](), [engine](), [open](), [postgres](), [public-beta](), [storage](), [storage-engine](), [tables](), [update]() ### AI overview OrioleDB is in public beta on Supabase plans as an alternative Postgres storage engine, but it is not ready for production and has no SLA. It replaces heap versions and dead tuples with an automatically reclaimed undo log; stores rows in the primary-key index; and uses copy-on-write checkpoints, row-level WAL, and lock-free page reads. These mechanisms can reduce vacuum work, index writes, lookup reads, and checkpoint I/O. In Supabase's published TPC-C-derived workload, OrioleDB led throughput per dollar when data exceeded memory; on an 8XL instance it reached about 1.8x heap throughput. With data in memory the advantage was smaller, and heap led at Small. Results depend on workload: frequent updates or deletes, large datasets, concurrency, and primary-key lookups may benefit more; read-mostly in-memory workloads or experimental non-B-tree indexes may not. Benchmark your workload. OrioleDB must be selected when creating a project, cannot be added or removed later, and lacks Point-in-Time Recovery. The beta also has unsupported commands and extensions, and is not available in self-hosted Supabase. ### Source excerpt OrioleDB, an open-source storage engine for Postgres, is in public beta on every Supabase plan. ## When Can a Learned Design Surrogate Be Trusted in a Self-Driving Microfluidics Laboratory? An Offline Closed-Loop Framework Built and Tested on Six Public Tables DevFeed: [When Can a Learned Design Surrogate Be Trusted in a Self-Driving Microfluidics Laboratory? An Offline Closed-Loop Framework Built and Tested on Six Public Tables]() Original publisher: [Read original article]() Author: Zhiyuan Zhang Published: 2026-10-28T04:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [model architecture](), [ML model versioning](), [A/B Testing]() Tags: [3](), [abstract](), [acquisition](), [adapting](), [all](), [arxiv](), [beats](), [better](), [boosting](), [budget](), [can](), [chemistry](), [cs-lg](), [design](), [driving](), [framework](), [laboratory](), [offline](), [public](), [self-driving](), [tables]() ### AI overview Across six public droplet-microfluidic and liposome tables evaluated offline, warm-start residual boosting recovered surrogate performance within 20 target measurements in 94% of chemistry tiers, 90% of laboratory tiers, and 40% of device tiers; chemistry recovery fell to 27% under a strict noise-floor reading. In closed-loop simulations, proposing all at once and warm-starting after each measurement reached a 10% target in 2.80 measurements versus 3.31 for the reference, and in 5.72 versus 11.35 for random proposals on 301 hard units at 3% tolerance. These gains survived drift and shuffled-label controls and were concentrated in sparsely sampled tiers. No tested label-free acquisition rule survived shuffled-label controls, no interval-aware proposal or alternative model family performed better, and no diagnostic threshold beat continuing once escalation was priced. The work is entirely offline and claims no wet-lab validation; its results do not establish that any label-free statistic can certify individual candidates. ### Source excerpt arXiv:2610.36395v1 Announce Type: new Abstract: Self-driving laboratories couple a learned design surrogate to an instrument, and on every new target domain it must answer four questions: when it can be trusted, how many target measurements it needs, which to make first, and when to stop adapting, switch models or escalate. We state a framework making each measurable (a competence ratio, a budget $k^*(\varepsilon)$, a time-to-target and a priced decision) and instantiate it without wet experiments on six public droplet-microfluidic and liposome tables as offline laboratories: 107 leave-one-group tiers whose rows are hidden labels. A sealed, pre-registered programme (two sealed negative validations and a pre-registered test on three more tables) and seven frozen closed-loop protocols, each run twice, yield 23 findings. Shift cost is stratified by shift type; no label-free statistic certifies individual candidates; random-split model-family rankings predicted neither the sealed nor the loop rankings. Warm-start residual boosting returns a surrogate to $1.25\times$ its development reference within 20 target rows in 94% of chemistry tiers (27% under a strict noise-floor reading), 90% of laboratory and 40% of device tiers, and a budget planned from the shift type on other tables is not measurably worse on a new one. In the loop, one gain survives drift and shuffled-label controls on all tiers, carried by the sparsely sampled ones: proposing at once and warm-starting on every measurement reaches a 10% target in 2.80 measurements against 3.31 for the reference, and in 5.72 against 11.35 for random proposals on 301 hard units at a 3% tolerance. No label-free acquisition rule survives the shuffled-label control, no interval-aware proposal or other model family does better, and no threshold rule on the tested diagnostics beats always continuing once escalation is priced. No wet-lab validation is claimed. ## Beyond LWE: Post-Quantum Assumptions under a Counterfactual Break of the Dihedral Coset Problem DevFeed: [Beyond LWE: Post-Quantum Assumptions under a Counterfactual Break of the Dihedral Coset Problem]() Original publisher: [Read original article]() Author: Ramses Fernandez Published: 2026-10-28T04:00:00Z Content type: article Language: en Sources: [arXiv: Cryptography and Security]() Topics: [Post-quantum cryptography](), [Cryptography](), [Provable security]() Tags: [cs-cr](), [lattice](), [lwe](), [ml-kem](), [module](), [post-quantum](), [quantum](), [reduction](), [ring](), [scheme](), [security](), [survey](), [tables]() ### AI overview The survey finds that even a noiseless cyclic DCP solver could enable lattice algorithms at some polynomial approximation factors, but quantitative consequences depend on the solver's guarantees. It distinguishes three DCP solver models, composes reductions with explicit sample, state-preparation, and success conditions, and compares their sufficient guarantees with more than thirty assumptions. Its audit finds that Wen and Zheng's printed state-conversion bound does not certify parameters at the ML-KEM ring and modulus. It also separates rigorous consequences from heuristic comparisons and unverified applications, and emphasizes that solving a proof assumption does not itself establish a scheme attack. The abstract says a proposed repair to Chen's withdrawn LWE algorithm would reach a limited regime already covered under a specified failure-exponent hypothesis, not a deployed parameter set. Classic McEliece's summarized classical structural attacks are separate from DCP. ### Source excerpt arXiv:2610.36731v1 Announce Type: new Abstract: A refuted 2026 claim of an efficient quantum algorithm for the Dihedral Coset Problem (DCP) motivates this survey of the consequences of a genuine breakthrough. We distinguish a noiseless cyclic coset-state solver, a solver with a prescribed failure exponent, and an extrapolated-state solver. Regev's rare-corruption observation already makes the first hypothesis sufficient for lattice algorithms at some polynomial approximation factors; the witnessing algorithm determines the quantitative bounds. We compose unstructured reductions with explicit sample, state-preparation and success conditions, and compare their sufficient guarantees with more than thirty lattice and non-lattice assumptions. We restate the structured state conversion of Wen and Zheng and audit its forward parameter certificate; the printed bound supplies no certificate at the ML-KEM ring and modulus. We use an explicit affine embedding for heuristic gap estimates, explain the lossiness connection from decision NTRU to search Ring-LWE, and distinguish ordinary from module uniqueness. Chen's withdrawn 2024 quantum algorithm for LWE and a proposed repair of its post-processing would, if correct, reach only a many-sample, small-noise LWE regime that the failure-exponent hypothesis already covers for exponents below $1/2$, and no deployed parameter set. Tables separate rigorous consequences, heuristic comparisons and unverified applications, and distinguish solving a proof assumption from attacking a scheme. Classical structural attacks on Classic McEliece, which do not involve DCP but put the concrete security of the scheme under revision, are summarised here and analysed in detail in a companion paper. The survey closes with open questions and a scenario-planning checklist that requires a complete efficient reduction. ## VDE Bench: Evaluating The Capability of Image Editing Models to Modify Visual Documents DevFeed: [VDE Bench: Evaluating The Capability of Image Editing Models to Modify Visual Documents]() Original publisher: [Read original article]() Author: Hongzhu Yi, Yujia Yang, Yuanxiang Wang, Tong Li, Zhenyu Guan, Tianyu Zong, Jiahuan Chen, Chenxi Bao, Tiankun Yang, Haopeng Jin, Yixuan Yuan, Xinming Wang, Tao Yu, Ruilin Gao, Ruiwen Tao, Haijin Liang, Published: 2026-10-28T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [image editing](), [Image]() Tags: [bench](), [benchmark](), [cs-ai](), [cs-cv](), [cs-mm](), [evaluation](), [image-editing](), [images](), [instructions](), [layout](), [ocr](), [protocol](), [region](), [regions](), [tables](), [text]() ### AI overview VDE Bench evaluates instruction-guided editing of Chinese and English document images, where local changes must fit tightly coupled text, tables, and layout. It contains 3,506 samples: 1,578 text-region edits and 1,928 table-region edits, paired with instructions, reference images, annotated edit regions, and structured OCR outputs. Its protocol combines region-level and full-page measurements of textual correctness, structural fidelity, and visual content preservation, aiming to diagnose failures that arise when edits affect surrounding document context. ### Source excerpt arXiv:2602.00122v5 Announce Type: replace-cross Abstract: Image editing models can modify visual content through natural language, but editing dense documents requires precise local changes amid tightly coupled text, tables, and layout. We introduce VDE Bench, an evaluation resource for studying instruction-guided editing of Chinese and English document images. VDE Bench contains 3,506 instruction-guided image editing samples, including 1,578 text-region edits and 1,928 table-region edits. The benchmark pairs editing instructions and reference images with annotated editing regions and structured OCR outputs. Its evaluation protocol combines region-level and full-page measurements to assess textual correctness, structural fidelity, and visual content preservation. By focusing on the interaction between local edits and their surrounding document context, VDE Bench complements existing visual text editing benchmarks and provides a resource for diagnosing document-specific editing failures. ## Converting Snowflake Native Tables to Iceberg: Part 2 DevFeed: [Converting Snowflake Native Tables to Iceberg: Part 2]() Original publisher: [Read original article]() Author: Leo Pasta Published: 2026-10-27T14:01:03Z Content type: tutorial Language: en Sources: [Snowflake Builders]() Topics: [Apache Iceberg](), [copy]() Tags: [2](), [all](), [also](), [apache-iceberg](), [approach](), [article](), [assets](), [can](), [capture](), [change](), [cleanup](), [consumer](), [conversion](), [copy](), [counts](), [covers](), [cross](), [data-engineering](), [data-lakehouse](), [data-migration](), [iceberg](), [native](), [snowflake](), [tables]() ### AI overview The guide recommends side-by-side conversion: keep the native table live for validation, then cut over with a controlled rename. Freeze writes, capture grants and policies, drain streams (unconsumed stream data is lost), and verify permissions. Populate the Iceberg table either by exporting Parquet and registering files with COPY INTO LOAD_MODE=ADD_FILES_COPY, which is more efficient but limited to specified basic column types and does not support partitioned tables, or by CTAS for other types. Restore grants, policies and tags, then compare row counts and HASH_AGG values and smoke-test representative consumers. HASH_AGG requires explicit column lists and does not accept GEOGRAPHY or GEOMETRY without ST_ASWKB; equality checks are needed for certainty. Recreate streams after cutover. Keep the renamed native table as rollback fallback; reverse the rename and restore write privileges if needed, then drop it after an agreed retention period. ### Source excerpt Part 2 of 2 -- with the pre-conversion decisions covered in part 1, this article covers conversion execution, data validation, cutover and rollback. Conversion strategy: Side-by-side Other than for initial tests and proofs of concept, side-by-side conversion is generally the right approach: Create the Iceberg table in a parallel schema, fully validate it while the native table remains live, and swap it in via a controlled rename sequence. The conversion follows five sequential steps: Pre-conversion: - Capture grants and policies - Plan and line up smoke test assets - Freeze writes Conversion - Create iceberg table (i.e. the iceberg version of the original table being converted) - Copy the data - Restore grants, policies and tags Validation (row counts, HASH_AGG, consumer smoke-test) Cutover (swap the original native table with its iceberg equivalent) Cleanup Pre-conversion tasks Confirm that you have the tools and scripts needed to smoke-test each table consumer. See "Consumer tool smoke-test" below. Generate and validate scripts to reapply all grants, tags, fine-grained access policies (like masking and row-access) on the table(s) being migrated Remove write permissions on the native table: revoke INSERT, UPDATE, DELETE and TRUNCATE while preserving SELECT (note that if the table owner is used for write, you might need to change the owner or pause the pipelines). Drain all streams on the table(s) being converted and drop them afterwards. A stream's offset can't be carried over to the new Iceberg table, so anything left unconsumed at conversion time is lost. Also confirm that the target schema exists (if not the same as the source table) and the role performing the migration has: - OWNERSHIP on source table and target schema (required for cross-schema rename) - CREATE ICEBERG TABLE permissions on the target schema - USAGE on the external volume, databases and schemas involved - If loading data via export and import (option 1), USAGE rights will be needed on the stage ## Why Faster Algorithms Aren't Enough to Build High-Performance Data Platforms DevFeed: [Why Faster Algorithms Aren't Enough to Build High-Performance Data Platforms]() Original publisher: [Read original article]() Author: Megaladata Published: 2026-10-27T13:43:41Z Content type: article Language: en Sources: [HackerNoon]() Topics: [systems](), [High-Performance Computing](), [database-optimization](), [in-memory database]() Tags: [2](), [2017](), [3](), [about](), [aggregation](), [algorithm](), [algorithms](), [browser](), [build](), [cpu](), [data](), [data-engineering](), [data-platforms](), [data-visualization](), [engine](), [faster](), [good-company](), [high-performance](), [in-memory-computing](), [memory](), [memory-management](), [node](), [parallel-computing](), [performance](), [performance-optimization](), [platforms](), [software-architecture](), [tables]() ### AI overview Megaladata's performance approach treats speed as an end-to-end architectural property: it reduces data movement and memory use across the workflow, computation, and browser. Nodes share a common access interface while using specialized representations; sorting can store row indexes instead of copying a table, and calculated columns can be evaluated on demand. The engine also processes data in blocks, uses TCMalloc alongside fixed-size allocators for suitable workloads, and runs parallelizable tasks by default while managing worker lifecycles to release resources. Python code runs in separate processes so independent workflow branches can execute concurrently, at the cost of per-node data-transfer overhead. For display, the server sends table windows and decimates chart data instead of transferring entire datasets. The article emphasizes tradeoffs: splitting parallel work is easier than merging results efficiently, and optimizing one node may not optimize the whole workflow. ### Source excerpt Why some tables should never exist: how a data engine cuts copies, memory use, and wasted CPU, from node-to-node access to what the browser actually draws. ## Certified Split Windows for Parallel Lexing: Recovering Boundaries Where No Byte Certifies DevFeed: [Certified Split Windows for Parallel Lexing: Recovering Boundaries Where No Byte Certifies]() Original publisher: [Read original article]() Author: Nicklas Nidh\\"ogg Published: 2026-10-27T04:00:00Z Content type: article Language: en Sources: [arXiv: Distributed Computing]() Topics: [Automaton](), [Finite-state machine]() Tags: [certificate](), [cs-dc](), [cs-fl](), [cs-pl](), [lexer](), [local](), [model](), [parallel](), [profile](), [pspace](), [scanner](), [semantic](), [serial](), [split](), [stream](), [string](), [tables](), [token](), [using](), [window](), [windows]() ### AI overview The paper extends certified parallel-lexing boundaries from single bytes to bounded windows that reveal where the token covering the window's final byte began. It models possible maximal-munch scanner histories and decides reachability exactly within a finite quotient: positive results are sound semantic certificates for flat, completely tokenizable input, while negative results are limited by the conservative model. Certificates depend on an asserted occurrence witness and may otherwise be vacuous. In 400 random token sets, 322 of 337 sets with no byte certificate gained a witnessed window; every exact-empty case in the predecessor study gained one of two to four bytes. Analysis uses compiled tables once after automaton construction, without input. The existence problem is PSPACE-complete, and shortest windows can be exponentially long in the square root of the number of live states. ### Source excerpt arXiv:2608.09761v3 Announce Type: replace-cross Abstract: A certified split point lets a parallel lexer cut unlexed input at a single byte with the serial token stream provably preserved, but several conventional token sets in the predecessor's controlled study certify no byte once string, comment, or whitespace-run forms are included (arXiv:2608.03473v2). We generalize from a byte to a bounded window, a byte string after which the position where the current token began is known, regardless of surrounding context. We certify the directly usable form of that recovery, namely that the token covering the window's final byte begins at the reported origin. The certificate is conditional on occurrence and may be vacuous; every applicability figure counts only windows carrying an asserted completely tokenizable occurrence witness. We give a conservative model of a maximal-munch scanner's possible histories across a window, prove it sound, and decide reachability in that model exactly by exhausting a finite quotient of its reachable configurations. Within the flat, completely tokenizable scope, model-positive answers are semantic certificates; negatives are relative to the model, which deliberately refuses some windows a greedy scanner would allow. Whether the model certifies any window, plain or witnessed, is PSPACE-complete, and a shortest window can need $2^{\Omega(\sqrt{n})}$ bytes over n live states. Two smaller searches decide plain model existence, the second shipped in release 2.2.0 of munch. In a sample of 400 random token sets, 322 of the 337 sets certifying no byte gain a witnessed window, with zero inconclusive searches, and every exact-empty row of the predecessor's study gains a witnessed window of two to four bytes. The analysis runs once after automaton construction, using only the compiled tables and no input. A boundary profile marks each gap of a window forced, crossed or open, and a window-local cut is sound exactly at forced gaps. [Next page]()