# easier

Published articles for easier.

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## Optimal In-context Adaptivity and Distributional Robustness of Transformers

DevFeed: [Optimal In-context Adaptivity and Distributional Robustness of Transformers](<https://devfeed.tech/articles/optimal-in-context-adaptivity-and-distributional-robustness-of-transformers-123074.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2510.23254>)

Author: Tianyi Ma, Tengyao Wang, Richard J. Samworth

Published: 2026-11-09T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Transformers](<https://devfeed.tech/topics/transformers.md>)

Tags: [convergence](<https://devfeed.tech/tags/convergence.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [easier](<https://devfeed.tech/tags/easier.md>), [faster](<https://devfeed.tech/tags/faster.md>), [learning](<https://devfeed.tech/tags/learning.md>), [lower-bounds](<https://devfeed.tech/tags/lower-bounds.md>), [math-st](<https://devfeed.tech/tags/math-st.md>), [pretraining](<https://devfeed.tech/tags/pretraining.md>), [regression](<https://devfeed.tech/tags/regression.md>), [robustness](<https://devfeed.tech/tags/robustness.md>), [shift](<https://devfeed.tech/tags/shift.md>), [stat-ml](<https://devfeed.tech/tags/stat-ml.md>), [stat-th](<https://devfeed.tech/tags/stat-th.md>), [transformers](<https://devfeed.tech/tags/transformers.md>)

### AI overview

The paper proves that a sufficiently large Transformer pretrained on enough tasks drawn from a mixture of difficulty levels can attain the optimal convergence rate for each fixed test difficulty, even when the test distribution shifts within a bounded chi-squared divergence from that difficulty's pretraining component. The results cover nonparametric regression with random smoothness and multi-index models with random smoothness and effective dimension. They also show that an estimator given the test distribution cannot achieve a faster expected-risk rate, a guarantee the authors describe as more appropriate than minimax lower bounds.

### Source excerpt

arXiv:2510.23254v5 Announce Type: replace-cross Abstract: We study in-context learning problems where a Transformer is pretrained on tasks drawn from a mixture distribution $\pi=\sum_{\alpha\in\mathcal{A}} \lambda_{\alpha} \pi_{\alpha}$, called the pretraining prior, in which each mixture component $\pi_{\alpha}$ is a distribution on tasks of a specific difficulty level indexed by $\alpha$. Our goal is to understand the performance of the pretrained Transformer when evaluated on a different test distribution $\mu$, consisting of tasks of fixed difficulty $\beta\in\mathcal{A}$, and with potential distribution shift relative to $\pi_\beta$, subject to the chi-squared divergence $\chi^2(\mu,\pi_{\beta})$ being at most $\kappa$. In particular, we consider nonparametric regression problems with random smoothness, and multi-index models with both random smoothness and random effective dimension. We prove that a large Transformer pretrained on sufficient data achieves the optimal rate of convergence corresponding to the difficulty level $\beta$, uniformly over test distributions $\mu$ in the chi-squared divergence ball. Thus, the pretrained Transformer is able to achieve faster rates of convergence on easier tasks and is robust to distribution shift at test time. Finally, we prove that even if an estimator had access to the test distribution $\mu$, the convergence rate of its expected risk over $\mu$ could not be faster than that of our pretrained Transformers, thereby providing a more appropriate optimality guarantee than minimax lower bounds.

## 17 Simple Raspberry Pi Tricks That Save You Time

DevFeed: [17 Simple Raspberry Pi Tricks That Save You Time](<https://devfeed.tech/articles/17-simple-raspberry-pi-tricks-that-save-you-time-121921.md>)

Original publisher: [Read original article](<https://raspberrytips.com/raspberry-pi-tricks-that-save-time/>)

Author: Usman Qamar

Published: 2026-11-07T05:00:00Z

Content type: tutorial

Language: en

Sources: [RaspberryTips](<https://devfeed.tech/sources/raspberrytips.md>)

Topics: [Raspberry Pi](<https://devfeed.tech/topics/raspberry-pi.md>), [Tutorial](<https://devfeed.tech/topics/tutorial.md>)

Tags: [17](<https://devfeed.tech/tags/17.md>), [but](<https://devfeed.tech/tags/but.md>), [can](<https://devfeed.tech/tags/can.md>), [easier](<https://devfeed.tech/tags/easier.md>), [everyday](<https://devfeed.tech/tags/everyday.md>), [i](<https://devfeed.tech/tags/i.md>), [inspiration](<https://devfeed.tech/tags/inspiration.md>), [little](<https://devfeed.tech/tags/little.md>), [make](<https://devfeed.tech/tags/make.md>), [my](<https://devfeed.tech/tags/my.md>), [over](<https://devfeed.tech/tags/over.md>), [pi](<https://devfeed.tech/tags/pi.md>), [pretty](<https://devfeed.tech/tags/pretty.md>), [raspberry](<https://devfeed.tech/tags/raspberry.md>), [raspberry-pi](<https://devfeed.tech/tags/raspberry-pi.md>), [save](<https://devfeed.tech/tags/save.md>), [simple](<https://devfeed.tech/tags/simple.md>), [small](<https://devfeed.tech/tags/small.md>), [tasks](<https://devfeed.tech/tags/tasks.md>), [time](<https://devfeed.tech/tags/time.md>), [tricks](<https://devfeed.tech/tags/tricks.md>)

### AI overview

The article presents Raspberry Pi time savers across the terminal, Raspberry Pi OS, and recurring setup tasks. Terminal tips include TAB completion, Ctrl+R history search, `sudo !!` to rerun the last command with elevated privileges, piping output through `grep`, and using `watch` for repeated monitoring. On the desktop, it recommends configuring shortcuts and pinned apps, bookmarking web interfaces, using Raspberry Pi Imager customization to reuse credentials and connectivity settings, and accessing another computer's files through the file manager's SFTP address. For repeated work, it suggests aliases for short commands and scripts for multi-step tasks, adding script folders to PATH, predictable hostnames and SSH aliases, and SSH keys for password-free connections and automation. It also recommends saving SD card images for projects that may need restoring, while noting image creation takes time.

### Source excerpt

Over the years, I've picked up a lot of little Raspberry Pi tricks to make everyday tasks easier. Most of them are pretty simple, but that's what makes them so useful. I've put together 17 of my favorites, and I'm sure you'll find a few that save you time too. Small Raspberry Pi tricks can...

## BrainPFN: Amortized Brain-Behaviour Prediction via Prior-Data Fitted Networks

DevFeed: [BrainPFN: Amortized Brain-Behaviour Prediction via Prior-Data Fitted Networks](<https://devfeed.tech/articles/brainpfn-amortized-brain-behaviour-prediction-via-prior-data-fitted-networks-120138.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2611.06934>)

Author: Carlo Alberto Barbano (MIND), Demian Wassermann (MIND)

Published: 2026-11-06T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>), [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [AI Foundation Models](<https://devfeed.tech/topics/ai-foundation-models.md>), [Open Data](<https://devfeed.tech/topics/open-data.md>)

Tags: [abstract](<https://devfeed.tech/tags/abstract.md>), [approach](<https://devfeed.tech/tags/approach.md>), [arxiv](<https://devfeed.tech/tags/arxiv.md>), [beats](<https://devfeed.tech/tags/beats.md>), [behaviour](<https://devfeed.tech/tags/behaviour.md>), [brain](<https://devfeed.tech/tags/brain.md>), [can](<https://devfeed.tech/tags/can.md>), [code](<https://devfeed.tech/tags/code.md>), [connectivity](<https://devfeed.tech/tags/connectivity.md>), [context](<https://devfeed.tech/tags/context.md>), [continuous](<https://devfeed.tech/tags/continuous.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [developing](<https://devfeed.tech/tags/developing.md>), [diagnosis](<https://devfeed.tech/tags/diagnosis.md>), [domain](<https://devfeed.tech/tags/domain.md>), [easier](<https://devfeed.tech/tags/easier.md>), [foundation](<https://devfeed.tech/tags/foundation.md>), [networks](<https://devfeed.tech/tags/networks.md>), [prediction](<https://devfeed.tech/tags/prediction.md>), [q-bio-nc](<https://devfeed.tech/tags/q-bio-nc.md>)

### AI overview

BrainPFN predicts behavioral scores from functional MRI using a single forward pass over a labeled context, avoiding separate per-score or per-cohort fitting. It is meta-trained using a domain-specific prior built from about 7,000 real functional connectivity matrices and one billion synthetic training samples. The abstract reports that across four independent cohorts it outperformed Kernel Ridge Regression (KRR) in small-sample prediction and led recent brain foundation models trained at four to nine times larger scale. Its training pool is smaller and publicly released with code and checkpoints; these results are limited to the reported cohorts and comparisons.

### Source excerpt

arXiv:2611.06934v1 Announce Type: cross Abstract: The standard approach to predicting behavioral phenotypes from functional MRI is represented by Kernel Ridge Regression (KRR) applied on functional connectivity (FC) matrices. However, this approach is limited: separate models must be fit and tuned for every score and cohort, they do not transfer across datasets, and accuracy degrades sharply when only a handful of subjects are available, as is typical of clinical or cognitive studies. Despite an increasing interest in developing brain foundation models, none has consistently outperformed KRR on behaviour prediction; most reported gains come from easier targets such as age, sex, or diagnosis rather than continuous behavioural scores. We introduce BrainPFN, a Prior Data Fitted Network for brain behaviour regression. BrainPFN is meta-trained on a domain-specific prior built from ~7k real FC matrices, from which we sample one billion synthetic training samples. At inference, a labelled context is simply passed through the network in a single forward pass, with no per-score or per-cohort refitting required. Across four independent cohorts, BrainPFN beats KRR on small sample prediction, and leads recent foundation models pre-trained at four to nine times larger scale. Our meta-training pool, aggregated from open repositories, is a fraction of the size of the corpora behind competing models, most of which sit behind restrictive data use agreements such as UK Biobank. We release this pool publicly alongside code and checkpoints, showing that a modest, openly available pool can match or beat models trained at Biobank scale.

## On the Generation Order in GFlowNets

DevFeed: [On the Generation Order in GFlowNets](<https://devfeed.tech/articles/on-the-generation-order-in-gflownets-111607.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.41648>)

Author: Timofei Gritsaev, Evgeny Katasonov, Ildus Sadrtdinov, Esmeralda S. Whitammer, Dmitry Vetrov

Published: 2026-11-02T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [annotations](<https://devfeed.tech/tags/annotations.md>), [component](<https://devfeed.tech/tags/component.md>), [conditional](<https://devfeed.tech/tags/conditional.md>), [convergence](<https://devfeed.tech/tags/convergence.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [distributions](<https://devfeed.tech/tags/distributions.md>), [easier](<https://devfeed.tech/tags/easier.md>), [faster](<https://devfeed.tech/tags/faster.md>), [generation](<https://devfeed.tech/tags/generation.md>), [gradient](<https://devfeed.tech/tags/gradient.md>), [learn](<https://devfeed.tech/tags/learn.md>), [learning](<https://devfeed.tech/tags/learning.md>), [network](<https://devfeed.tech/tags/network.md>), [neural-network](<https://devfeed.tech/tags/neural-network.md>), [noise](<https://devfeed.tech/tags/noise.md>), [order](<https://devfeed.tech/tags/order.md>), [orders](<https://devfeed.tech/tags/orders.md>), [problems](<https://devfeed.tech/tags/problems.md>), [ratio](<https://devfeed.tech/tags/ratio.md>), [recipe](<https://devfeed.tech/tags/recipe.md>)

### AI overview

In controlled problems, the order in which GFlowNets add discrete components affects convergence, and standard training struggles to discover useful orders. Learning-Order GFlowNet learns interpretable generation orders without order annotations and improves convergence on those problems. The authors also find that orders linked to faster convergence have higher gradient signal-to-noise ratios at comparable validation loss, suggesting SNR may help explain how useful orders emerge and characterize them. The evidence provided is an abstract, so it does not specify the method's implementation or broader applicability.

### Source excerpt

arXiv:2610.41648v1 Announce Type: new Abstract: Sampling from discrete distributions specified by unnormalised probability functions is a classical statistical problem. GFlowNets address this problem by constructing objects sequentially, adding one discrete component at a time in either a predefined or a learned order. The choice of order determines which conditional distributions the neural network must learn, some of which may be easier to learn than others. Yet whether existing GFlowNet algorithms discover interpretable generation orders and make use of them during training remains unexplored. We introduce controlled problems where generation order is experimentally shown to affect convergence and standard GFlowNet training struggles to discover useful orders. To address this difficulty, we develop Learning-Order GFlowNet, a training recipe that learns interpretable generation orders without order annotations and improves convergence on these problems. Finally, we investigate gradient signal-to-noise ratio (SNR) as a possible explanation for order emergence: orders associated with faster convergence exhibit higher SNR at comparable validation loss, suggesting both a mechanism favouring their emergence and an SNR-based characterisation of a good generation order.

## What's new in Red Hat OpenShift migration advisor: Clearer reports and smarter planning

DevFeed: [What's new in Red Hat OpenShift migration advisor: Clearer reports and smarter planning](<https://devfeed.tech/articles/what-s-new-in-red-hat-openshift-migration-advisor-clearer-reports-and-smarter-planning-113545.md>)

Original publisher: [Read original article](<https://www.redhat.com/en/blog/whats-new-red-hat-openshift-migration-advisor-clearer-reports-and-smarter-planning>)

Author: Margot Menestrot

Published: 2026-11-02T00:00:00Z

Content type: release

Language: en

Sources: [Red Hat Blog](<https://devfeed.tech/sources/red-hat-blog.md>)

Topics: [virtualization](<https://devfeed.tech/topics/virtualization.md>), [virtual machines](<https://devfeed.tech/topics/virtual-machines.md>), [redhat](<https://devfeed.tech/topics/redhat.md>), [Data analysis](<https://devfeed.tech/topics/data-analysis.md>), [data migrations](<https://devfeed.tech/topics/data-migrations.md>)

Tags: [analysis](<https://devfeed.tech/tags/analysis.md>), [assessment](<https://devfeed.tech/tags/assessment.md>), [cluster](<https://devfeed.tech/tags/cluster.md>), [conversations](<https://devfeed.tech/tags/conversations.md>), [details](<https://devfeed.tech/tags/details.md>), [easier](<https://devfeed.tech/tags/easier.md>), [hat](<https://devfeed.tech/tags/hat.md>), [helps](<https://devfeed.tech/tags/helps.md>), [here](<https://devfeed.tech/tags/here.md>), [here-s](<https://devfeed.tech/tags/here-s.md>), [heres](<https://devfeed.tech/tags/heres.md>), [information](<https://devfeed.tech/tags/information.md>), [migration](<https://devfeed.tech/tags/migration.md>), [openshift](<https://devfeed.tech/tags/openshift.md>), [planning](<https://devfeed.tech/tags/planning.md>), [red](<https://devfeed.tech/tags/red.md>), [red-hat](<https://devfeed.tech/tags/red-hat.md>), [red-hat-openshift](<https://devfeed.tech/tags/red-hat-openshift.md>), [reports](<https://devfeed.tech/tags/reports.md>), [what-s-new](<https://devfeed.tech/tags/what-s-new.md>)

### AI overview

Red Hat OpenShift migration advisor now supports more ongoing VMware migration planning in-product. Reports include cluster and assessment details, OS support by certification tier, and application context for the environment and individual VMs. Planning updates include a refreshed cluster complexity form, storage offload and rightsizing improvements, and tools to label, group, or exclude VMs; standalone sizing and TCO workflows are available to partners only. Users can refresh assessments from the same discovery appliance without new setup and compare historical reports for changes. Exports include selectable CSV metrics, PDF reports, PNG charts, and HTML reports. The excerpt also describes clearer credential storage and disconnect flows, including notices about retained or removed data.

### Source excerpt

Red Hat OpenShift migration advisor helps you assess your VMware inventory of virtual machines (VMs) and plan migrations to Red Hat OpenShift Virtualization. Over the past few months, we've focused on making assessments easier to read and share, and more useful when you're working across multiple inventories, so more of the analysis stays in the product.Here's what's new.Get more in the assessment report--and share what matters Assessment reports now show more of the information you need for migration conversations.Additional cluster and assessment details in the reportClearer operati

## How to Navigate Different Personalities at Work

DevFeed: [How to Navigate Different Personalities at Work](<https://devfeed.tech/articles/how-to-navigate-different-personalities-at-work-107781.md>)

Original publisher: [Read original article](<https://newsletter.bigtechcareers.com/p/working-with-different-personalities>)

Author: Prasad Rao

Published: 2026-10-29T15:02:06Z

Content type: article

Language: en

Sources: [Big Tech Careers](<https://devfeed.tech/sources/big-tech-careers.md>)

Topics: [engineering-culture](<https://devfeed.tech/topics/engineering-culture.md>), [meetings](<https://devfeed.tech/topics/meetings.md>), [Tech Lead](<https://devfeed.tech/topics/tech-lead.md>)

Tags: [2](<https://devfeed.tech/tags/2.md>), [2022](<https://devfeed.tech/tags/2022.md>), [3](<https://devfeed.tech/tags/3.md>), [about](<https://devfeed.tech/tags/about.md>), [academy](<https://devfeed.tech/tags/academy.md>), [acceptance](<https://devfeed.tech/tags/acceptance.md>), [adapting](<https://devfeed.tech/tags/adapting.md>), [alex](<https://devfeed.tech/tags/alex.md>), [align](<https://devfeed.tech/tags/align.md>), [all](<https://devfeed.tech/tags/all.md>), [also](<https://devfeed.tech/tags/also.md>), [approach](<https://devfeed.tech/tags/approach.md>), [architect](<https://devfeed.tech/tags/architect.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [can](<https://devfeed.tech/tags/can.md>), [easier](<https://devfeed.tech/tags/easier.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [make](<https://devfeed.tech/tags/make.md>), [others](<https://devfeed.tech/tags/others.md>), [work](<https://devfeed.tech/tags/work.md>)

### AI overview

Workplace friction can arise from different working styles rather than difficult personalities, and adapting how colleagues collaborate can help, especially in senior engineering roles. The article identifies four recurring differences: thinking aloud versus reflecting first, prioritizing the big picture versus detail, deciding quickly versus gathering evidence, and direct versus diplomatic feedback. It recommends describing observable behavior instead of labeling people, asking about work preferences, sharing your own, and making expectations explicit. For decisions, teams can agree on deadlines, needed evidence, risks that warrant delay, and whether choices are reversible. These adjustments should not excuse repeated disrespect or broken commitments. The article also suggests framing collaboration examples in interviews with the STAR method.

### Source excerpt

Why understanding how others work can make working together easier.

## Why Developers Will Have to Become Marketers -- and What Exactly They Should Learn

DevFeed: [Why Developers Will Have to Become Marketers -- and What Exactly They Should Learn](<https://devfeed.tech/articles/why-developers-will-have-to-become-marketers-and-what-exactly-they-should-learn-106136.md>)

Original publisher: [Read original article](<https://hackernoon.com/why-developers-will-have-to-become-marketers-and-what-exactly-they-should-learn?source=rss>)

Author: Volodymyr Khomichenko

Published: 2026-10-29T07:43:40Z

Content type: opinion

Language: en

Sources: [HackerNoon](<https://devfeed.tech/sources/hackernoon.md>)

Topics: [Learning](<https://devfeed.tech/topics/learning.md>), [Product Management](<https://devfeed.tech/topics/product-management.md>), [User Experience](<https://devfeed.tech/topics/user-experience.md>), [UI/UX Design](<https://devfeed.tech/topics/ui-ux-design.md>)

Tags: [accessible](<https://devfeed.tech/tags/accessible.md>), [acquisition](<https://devfeed.tech/tags/acquisition.md>), [ads](<https://devfeed.tech/tags/ads.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-tools](<https://devfeed.tech/tags/ai-tools.md>), [all](<https://devfeed.tech/tags/all.md>), [also](<https://devfeed.tech/tags/also.md>), [alternatives](<https://devfeed.tech/tags/alternatives.md>), [building](<https://devfeed.tech/tags/building.md>), [developers](<https://devfeed.tech/tags/developers.md>), [easier](<https://devfeed.tech/tags/easier.md>), [getting](<https://devfeed.tech/tags/getting.md>), [indie-dev](<https://devfeed.tech/tags/indie-dev.md>), [isn-t](<https://devfeed.tech/tags/isn-t.md>), [launch](<https://devfeed.tech/tags/launch.md>), [learn](<https://devfeed.tech/tags/learn.md>), [marketing](<https://devfeed.tech/tags/marketing.md>), [marketing-for-developers](<https://devfeed.tech/tags/marketing-for-developers.md>), [marketing-for-startups](<https://devfeed.tech/tags/marketing-for-startups.md>), [need](<https://devfeed.tech/tags/need.md>), [people](<https://devfeed.tech/tags/people.md>), [product-management](<https://devfeed.tech/tags/product-management.md>), [products](<https://devfeed.tech/tags/products.md>), [software-development](<https://devfeed.tech/tags/software-development.md>), [startup-marketing](<https://devfeed.tech/tags/startup-marketing.md>), [startups](<https://devfeed.tech/tags/startups.md>), [understand](<https://devfeed.tech/tags/understand.md>)

### AI overview

Building a product has become more accessible, but marketing that reaches and persuades customers has grown harder as content and product launches multiply. The article argues that early-stage technical founders cannot assume a marketer will resolve basic questions about customers, the problem, value, and differentiation. Developers need not master marketing specialties, but should learn to understand how people make decisions and see the product through users' eyes: talk with customers, listen to how they describe problems, observe product use, and learn how they solve the problem today. Marketing should begin before coding, with questions about audience, need, alternatives, switching reasons, and where to find customers. This user understanding can inform both product decisions and how its value is explained.

### Source excerpt

Building is getting easier, marketing isn't. Why developers launching products need to understand people and start marketing before launch.

## System76 Introducing AMD-Powered Linux Small PC

DevFeed: [System76 Introducing AMD-Powered Linux Small PC](<https://devfeed.tech/articles/system76-introducing-amd-powered-linux-small-pc-105253.md>)

Original publisher: [Read original article](<https://www.phoronix.com/news/System76-Thelio-Prime-Mini-PC>)

Author: Michael Larabel

Published: 2026-10-28T17:32:46Z

Content type: news

Language: en

Sources: [Phoronix](<https://devfeed.tech/sources/phoronix.md>)

Topics: [Linux mini PC](<https://devfeed.tech/topics/linux-mini-pc.md>)

Tags: [amd](<https://devfeed.tech/tags/amd.md>), [amd-ryzen](<https://devfeed.tech/tags/amd-ryzen.md>), [assembly](<https://devfeed.tech/tags/assembly.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [better](<https://devfeed.tech/tags/better.md>), [but](<https://devfeed.tech/tags/but.md>), [can](<https://devfeed.tech/tags/can.md>), [cooling](<https://devfeed.tech/tags/cooling.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [ddr5](<https://devfeed.tech/tags/ddr5.md>), [desktop-linux](<https://devfeed.tech/tags/desktop-linux.md>), [easier](<https://devfeed.tech/tags/easier.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [graphics](<https://devfeed.tech/tags/graphics.md>), [just](<https://devfeed.tech/tags/just.md>), [linux](<https://devfeed.tech/tags/linux.md>), [linux-benchmarking](<https://devfeed.tech/tags/linux-benchmarking.md>), [linux-hardware-benchmarks](<https://devfeed.tech/tags/linux-hardware-benchmarks.md>), [linux-hardware-reviews](<https://devfeed.tech/tags/linux-hardware-reviews.md>), [linux-how-to](<https://devfeed.tech/tags/linux-how-to.md>), [linux-performance](<https://devfeed.tech/tags/linux-performance.md>), [linux-server-benchmarks](<https://devfeed.tech/tags/linux-server-benchmarks.md>), [major](<https://devfeed.tech/tags/major.md>), [mini](<https://devfeed.tech/tags/mini.md>), [mini-pc](<https://devfeed.tech/tags/mini-pc.md>), [open-source-graphics](<https://devfeed.tech/tags/open-source-graphics.md>), [pc](<https://devfeed.tech/tags/pc.md>), [phoronix](<https://devfeed.tech/tags/phoronix.md>), [phoronix-test-suite](<https://devfeed.tech/tags/phoronix-test-suite.md>), [small](<https://devfeed.tech/tags/small.md>), [ubuntu-benchmarks](<https://devfeed.tech/tags/ubuntu-benchmarks.md>), [ubuntu-hardware](<https://devfeed.tech/tags/ubuntu-hardware.md>)

### AI overview

System76's Thelio Prime is an 11-liter Linux desktop designed to be smaller than its Thelio Major and Mira while allowing room for ventilation, repairs, and upgrades. It supports AMD Ryzen 9000 processors, integrated Radeon graphics or discrete AMD/NVIDIA graphics, up to 96GB of DDR5 memory, and up to 8TB of NVMe storage. Dedicated thermal chambers serve the CPU and GPU. System76 says the chassis is made and the system assembled at its Colorado facility; the supplied text gives a launch date of 18 November.

### Source excerpt

System76 began teasing today the new Thelio Prime Desktop as an AMD-powered Linux mini PC that will begin shipping in November...

## 7 Checks That Make Form Bugs Easier to Reproduce Across Teams

DevFeed: [7 Checks That Make Form Bugs Easier to Reproduce Across Teams](<https://devfeed.tech/articles/7-checks-that-make-form-bugs-easier-to-reproduce-across-teams-102774.md>)

Original publisher: [Read original article](<https://www.sitepoint.com/7-checks-that-make-form-bugs-easier-to-reproduce-across-teams/>)

Author: gian undefined

Published: 2026-10-27T11:17:16Z

Content type: tutorial

Language: en

Sources: [SitePoint](<https://devfeed.tech/sources/sitepoint.md>)

Topics: [Responsive Design](<https://devfeed.tech/topics/responsive-design.md>), [Behavior-driven development](<https://devfeed.tech/topics/bdd.md>), [accessible required form field](<https://devfeed.tech/topics/accessible-required-form-field.md>), [Code review](<https://devfeed.tech/topics/code-review.md>), [Web Content Accessibility Guidelines](<https://devfeed.tech/topics/web-content-accessibility-guidelines.md>), [CI/CD](<https://devfeed.tech/topics/cicd.md>), [synthetic monitoring](<https://devfeed.tech/topics/synthetic-monitoring.md>), [Test-driven development](<https://devfeed.tech/topics/tdd.md>)

Tags: [2](<https://devfeed.tech/tags/2.md>), [3](<https://devfeed.tech/tags/3.md>), [absolute](<https://devfeed.tech/tags/absolute.md>), [accessibility](<https://devfeed.tech/tags/accessibility.md>), [accessible](<https://devfeed.tech/tags/accessible.md>), [account](<https://devfeed.tech/tags/account.md>), [alert](<https://devfeed.tech/tags/alert.md>), [align](<https://devfeed.tech/tags/align.md>), [allows](<https://devfeed.tech/tags/allows.md>), [also](<https://devfeed.tech/tags/also.md>), [animation](<https://devfeed.tech/tags/animation.md>), [animations](<https://devfeed.tech/tags/animations.md>), [announce](<https://devfeed.tech/tags/announce.md>), [api](<https://devfeed.tech/tags/api.md>), [bugs](<https://devfeed.tech/tags/bugs.md>), [css](<https://devfeed.tech/tags/css.md>), [developer-tools](<https://devfeed.tech/tags/developer-tools.md>), [easier](<https://devfeed.tech/tags/easier.md>), [form](<https://devfeed.tech/tags/form.md>), [html-css](<https://devfeed.tech/tags/html-css.md>), [java](<https://devfeed.tech/tags/java.md>), [make](<https://devfeed.tech/tags/make.md>), [reading](<https://devfeed.tech/tags/reading.md>), [teams](<https://devfeed.tech/tags/teams.md>)

### AI overview

Reproducible form bug reports need to preserve the UI state that caused the failure: starting conditions, action, response, observed and expected behavior, viewport, browser, revision, and permissions. The excerpt's seven checks include deterministic boundary fixtures; separating isolated component rendering (for example, Storybook args) from request lifecycle testing (such as Mock Service Worker or request interception), while retaining real API integration coverage; diagnosing layout constraints before changing CSS; and asserting form behavior before comparing Playwright screenshots. It also recommends self-contained review notes and accessibility checks for labels, keyboard use, focus, and error relationships. Tool choice should match the question: a rendered error check does not prove the server response triggers it, and a screenshot alone cannot verify preserved inputs or navigation. Screenshot baselines require a consistent environment and manual review; the proposed CSS layout is only an example, not a universal fix. This overview reflects the supplied excerpt.

### Source excerpt

null Continue reading 7 Checks That Make Form Bugs Easier to Reproduce Across Teams on SitePoint.

## Complementing DINO Features with Image Structure for Part Discovery

DevFeed: [Complementing DINO Features with Image Structure for Part Discovery](<https://devfeed.tech/articles/complementing-dino-features-with-image-structure-for-part-discovery-99595.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.32293>)

Author: Samyak Rawlekar, Nikhil Chowdary Paleti, Amey Gupta, Narendra Ahuja

Published: 2026-10-26T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>), [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [image recognition](<https://devfeed.tech/topics/image-recognition.md>), [clustering](<https://devfeed.tech/topics/clustering.md>), [Image](<https://devfeed.tech/topics/image.md>)

Tags: [abstract](<https://devfeed.tech/tags/abstract.md>), [approach](<https://devfeed.tech/tags/approach.md>), [arxiv](<https://devfeed.tech/tags/arxiv.md>), [background](<https://devfeed.tech/tags/background.md>), [based](<https://devfeed.tech/tags/based.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [clustering](<https://devfeed.tech/tags/clustering.md>), [coherent](<https://devfeed.tech/tags/coherent.md>), [combines](<https://devfeed.tech/tags/combines.md>), [confidence](<https://devfeed.tech/tags/confidence.md>), [contain](<https://devfeed.tech/tags/contain.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-cv](<https://devfeed.tech/tags/cs-cv.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [cub](<https://devfeed.tech/tags/cub.md>), [dino](<https://devfeed.tech/tags/dino.md>), [discovery](<https://devfeed.tech/tags/discovery.md>), [easier](<https://devfeed.tech/tags/easier.md>), [entropy](<https://devfeed.tech/tags/entropy.md>), [features](<https://devfeed.tech/tags/features.md>), [image](<https://devfeed.tech/tags/image.md>), [structure](<https://devfeed.tech/tags/structure.md>), [with](<https://devfeed.tech/tags/with.md>)

### AI overview

DINO's rigid patch grid can blur semantic part boundaries: experiments on over 26,000 foreground patches found that patches crossing boundaries have higher feature entropy and lower assignment confidence. Part-DINO addresses this without retraining DINO: it uses low-level image segmentation boundaries to form regions, pools DINO features within them, then groups the features to discover parts. The abstract reports structurally coherent assignments, better results than training-free baselines on CUB, CelebA, PartImageNet-OOD, and PartImageNet-Seg, and competitive performance against weakly supervised and unsupervised methods.

### Source excerpt

arXiv:2610.32293v1 Announce Type: cross Abstract: Self-supervised Vision Transformers (ViTs) like DINO encode rich semantics, enabling training-free segmentation of objects from the background and discovery of their parts via feature clustering. This paper focuses on part discovery: partitioning a foreground object into semantic parts. Existing approaches produce imprecise part boundaries. We trace this to the rigid ViT patch grid used in training DINO, where patches straddling boundaries contain pixels from multiple parts. While DINO's invariance-based training could absorb this pixel-level mixing into clean features, experiments with over 26K foreground patches show it does not: boundary-overlapping patches produce features with higher entropy and lower assignment confidence, blurring predicted part boundaries. Rather than make the massive effort of retraining DINO over geometrically coherent regions, an easier approach is to take boundaries directly from a boundary detector, which supplies the geometric information DINO's features lack. Motivated by this, we introduce Part-DINO, a training-free framework that combines boundaries from low-level image segmentation with DINO's semantic features. We use the boundaries to partition the image into regions, pool DINO features within each region, and group them to discover parts. Without any training, Part-DINO produces structurally coherent part assignments, outperforming training-free baselines across four standard benchmarks (CUB, CelebA, PartImageNet-OOD, PartImageNet-Seg) and remaining competitive with weakly-supervised and unsupervised methods.

## Regular Syndrome Decoding Is Easier Than Previously Estimated

DevFeed: [Regular Syndrome Decoding Is Easier Than Previously Estimated](<https://devfeed.tech/articles/regular-syndrome-decoding-is-easier-than-previously-estimated-94528.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.29175>)

Author: Jiseung Kim, Minkyu Kim, Changmin Lee, Hochang Lee

Published: 2026-10-22T04:00:00Z

Content type: news

Language: en

Sources: [arXiv: Cryptography and Security](<https://devfeed.tech/sources/arxiv-cryptography-and-security.md>)

Topics: [Cryptography](<https://devfeed.tech/topics/cryptography.md>), [computational complexity](<https://devfeed.tech/topics/computational-complexity.md>)

Tags: [attack](<https://devfeed.tech/tags/attack.md>), [attacks](<https://devfeed.tech/tags/attacks.md>), [cryptographic](<https://devfeed.tech/tags/cryptographic.md>), [cs-cr](<https://devfeed.tech/tags/cs-cr.md>), [decoding](<https://devfeed.tech/tags/decoding.md>), [easier](<https://devfeed.tech/tags/easier.md>), [hybrid](<https://devfeed.tech/tags/hybrid.md>), [regular](<https://devfeed.tech/tags/regular.md>), [security](<https://devfeed.tech/tags/security.md>)

### AI overview

A refined hybrid algebraic attack lowers estimated concrete hardness for many Regular Syndrome Decoding (RSD) parameter sets by coordinating zero-coordinate guesses and syndrome-based variable eliminations across regular blocks, balancing guessing costs while retaining equations for linearization. The reassessment finds at least one parameter set in each of Ferret, Wolverine, and committed VOLE below its 128-bit target, with losses approaching 67 bits. On pseudorandom correlation generator benchmarks, estimated attack costs improve by up to 29.77 bits over non-binary fields and 25.91 bits over binary fields; the revised 128-bit PCG sets of Liu et al. evaluated here are all estimated below 2^111 field operations.

### Source excerpt

arXiv:2610.29175v1 Announce Type: new Abstract: The Regular Syndrome Decoding (RSD) problem is a structured variant of classical Syndrome Decoding and underlies a growing number of efficient cryptographic constructions, including zero knowledge protocols, pseudorandom correlation generators, and MPC in the Head signatures. Although RSD is generally believed to be computationally hard, recent works have shown that algebraic attacks can outperform information set decoding on important RSD parameter regimes. Building on this line of work, we revisit the concrete hardness of RSD and show that many widely used parameter sets are substantially easier to solve than previously estimated. This improvement stems from a refined hybrid algebraic attack that exposes and removes a previously overlooked source of concrete complexity: the joint blockwise placement of zero coordinate guesses and syndrome based variable eliminations. Rather than treating the two guessing stages and linear elimination as largely separate choices, our method coordinates them across all regular blocks so that the guessing penalty is balanced while sufficient equations are retained for linearization. This gives a systematic block balancing principle for the entire guess reduce solve pipeline. We reassess several RSD based applications, including Ferret, Wolverine, and committed VOLE. Our estimates place at least one parameter set per application below its 128 bit target, with losses approaching 67 bits. On PCG benchmarks, we improve previous best attack costs by up to 29.77 and 25.91 bits over non binary and binary fields, respectively. Moreover, for the revised 128 bit PCG parameter sets of Liu et al. evaluated here, estimated attack costs are all below $2^{111}$ field operations. These results substantially revise the concrete security landscape of RSD and indicate that several existing parameter choices overestimate the hardness of their underlying RSD instances.

## Redesigning My Resources

DevFeed: [Redesigning My Resources](<https://devfeed.tech/articles/redesigning-my-resources-92826.md>)

Original publisher: [Read original article](<https://labs.ripe.net/author/antonella-de-bellis/redesigning-my-resources/>)

Author: Antonella De Bellis

Published: 2026-10-21T13:15:15Z

Content type: article

Language: en

Sources: [RIPE Labs](<https://devfeed.tech/sources/ripe-labs.md>)

Topics: [User interface design](<https://devfeed.tech/topics/ui-design.md>), [dashboards](<https://devfeed.tech/topics/dashboards.md>)

Tags: [attention](<https://devfeed.tech/tags/attention.md>), [automated](<https://devfeed.tech/tags/automated.md>), [better](<https://devfeed.tech/tags/better.md>), [easier](<https://devfeed.tech/tags/easier.md>), [guidance](<https://devfeed.tech/tags/guidance.md>), [help](<https://devfeed.tech/tags/help.md>), [internet](<https://devfeed.tech/tags/internet.md>), [internet-number-resources](<https://devfeed.tech/tags/internet-number-resources.md>), [keep](<https://devfeed.tech/tags/keep.md>), [make](<https://devfeed.tech/tags/make.md>), [manage](<https://devfeed.tech/tags/manage.md>), [members](<https://devfeed.tech/tags/members.md>), [ncc](<https://devfeed.tech/tags/ncc.md>), [needs](<https://devfeed.tech/tags/needs.md>), [number](<https://devfeed.tech/tags/number.md>), [operational](<https://devfeed.tech/tags/operational.md>), [practical](<https://devfeed.tech/tags/practical.md>), [resources](<https://devfeed.tech/tags/resources.md>), [ripe](<https://devfeed.tech/tags/ripe.md>), [ripe-ncc](<https://devfeed.tech/tags/ripe-ncc.md>), [understand](<https://devfeed.tech/tags/understand.md>)

### AI overview

The redesigned My Resources page aims to help RIPE NCC members understand resource registrations and identify what may need review. A tree view shows how allocations, sub-allocations and assignments relate; selecting a resource opens details on registration, utilisation, RIPE Database objects and routing. Automated checks flag examples such as unannounced registered assignments or ASNs absent from the BGP table, with explanations of what was detected and possible next steps. The interface distinguishes review items from neutral information: a missing exact route match may be intentional, so it should not automatically be treated as an error. Large hierarchies pose a performance and transparency challenge when checks require many API calls, and the design is still being developed. Planned member testing will assess navigation, checks, guidance and the connection to Registry Check.

### Source excerpt

We're redesigning the My Resources page in the LIR Portal to help RIPE NCC members better understand and manage their Internet number resources. Clearer hierarchies, automated checks and practical guidance aim to make it easier to see what needs attention and keep registrations up to date.

## Databricks AI Runtime and NVIDIA make it easier to customize open models with enterprise data

DevFeed: [Databricks AI Runtime and NVIDIA make it easier to customize open models with enterprise data](<https://devfeed.tech/articles/databricks-ai-runtime-and-nvidia-make-it-easier-to-customize-open-models-with-enterprise-data-92640.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/databricks-ai-runtime-nvidia-train-fine-tune-models>)

Author: Tejas Sundaresan; Hanlin Tang; Julia Powell; Joel Caruso (NVIDIA); Mayank Tahilramani (NVIDIA)

Published: 2026-10-21T11:30:00Z

Content type: release

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [databricks](<https://devfeed.tech/topics/databricks.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [AI Models](<https://devfeed.tech/topics/ai-models.md>), [Open Source Models & Datasets](<https://devfeed.tech/topics/open-source-models-datasets.md>), [Nemotron](<https://devfeed.tech/topics/nemotron.md>), [NeMo](<https://devfeed.tech/topics/nemo.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [NGC](<https://devfeed.tech/topics/ngc.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [advanced](<https://devfeed.tech/tags/advanced.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-workloads](<https://devfeed.tech/tags/ai-workloads.md>), [announcements](<https://devfeed.tech/tags/announcements.md>), [data](<https://devfeed.tech/tags/data.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [databricks-ai](<https://devfeed.tech/tags/databricks-ai.md>), [easier](<https://devfeed.tech/tags/easier.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [enterprise-data](<https://devfeed.tech/tags/enterprise-data.md>), [gpus](<https://devfeed.tech/tags/gpus.md>), [language](<https://devfeed.tech/tags/language.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [make](<https://devfeed.tech/tags/make.md>), [models](<https://devfeed.tech/tags/models.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open](<https://devfeed.tech/tags/open.md>), [platform](<https://devfeed.tech/tags/platform.md>), [platform-products-announcements](<https://devfeed.tech/tags/platform-products-announcements.md>), [power](<https://devfeed.tech/tags/power.md>), [runtime](<https://devfeed.tech/tags/runtime.md>), [with](<https://devfeed.tech/tags/with.md>)

### AI overview

Databricks AI Runtime (AIR) provides serverless NVIDIA GPUs for training and fine-tuning open models on governed enterprise data, with Databricks managing GPU clusters and networking. Developers can use NVIDIA NeMo, NeMo Automodel, NGC containers, custom Docker environments, and multi-node distributed training; Quantum InfiniBand and NCCL support communication across GPU nodes. Unity Catalog governs training-data access, while MLflow tracks experiments and GPU use. Lakeflow Jobs and Declarative Automation Bundles support repeatable workflows and CI/CD. The excerpt describes fine-tuning a Nemotron model on support tickets, evaluating it, then deploying it through Model Serving for real-time inference. AIR also supports a validated Kumo Anomaly workflow for time-series anomaly detection. Genie Code can help author and debug training scripts, with developers reviewing suggested code before execution. The described capabilities reduce infrastructure and setup work, while allowing teams to bring custom libraries and containers.

### Source excerpt

GPUs power today's most advanced AI workloads, from cutting-edge language models...

## Understanding the Benefits of Scaffolds through Computational Separations

DevFeed: [Understanding the Benefits of Scaffolds through Computational Separations](<https://devfeed.tech/articles/understanding-the-benefits-of-scaffolds-through-computational-separations-94250.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.27157>)

Author: Jingchu Gai, Adam Block, Andrej Risteski

Published: 2026-10-21T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [computational complexity](<https://devfeed.tech/topics/computational-complexity.md>), [Agent Harness](<https://devfeed.tech/topics/agent-harness.md>)

Tags: [abstract](<https://devfeed.tech/tags/abstract.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [arxiv](<https://devfeed.tech/tags/arxiv.md>), [base](<https://devfeed.tech/tags/base.md>), [call](<https://devfeed.tech/tags/call.md>), [can](<https://devfeed.tech/tags/can.md>), [capabilities](<https://devfeed.tech/tags/capabilities.md>), [capacity](<https://devfeed.tech/tags/capacity.md>), [chain-of-thought](<https://devfeed.tech/tags/chain-of-thought.md>), [class](<https://devfeed.tech/tags/class.md>), [closure](<https://devfeed.tech/tags/closure.md>), [competition](<https://devfeed.tech/tags/competition.md>), [complete](<https://devfeed.tech/tags/complete.md>), [compute](<https://devfeed.tech/tags/compute.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [easier](<https://devfeed.tech/tags/easier.md>), [external](<https://devfeed.tech/tags/external.md>), [fixed](<https://devfeed.tech/tags/fixed.md>), [gap](<https://devfeed.tech/tags/gap.md>), [harness](<https://devfeed.tech/tags/harness.md>), [inference](<https://devfeed.tech/tags/inference.md>)

### AI overview

Inference-time harnesses--procedures that call a model repeatedly and retain state--can outperform a student distilled from their traces, even with simple scaffolds and small models. The paper attributes this gap to representational limits: repeated calls can compute an NL-complete closure even when each call is a simple AC⁰ lookup, while a fixed-depth forward pass cannot do so under the widely believed conjecture NL ⊄ TC⁰. Experiments indicate the gap can be narrowed by increasing the student's capacity relative to the task, such as enlarging the model, simplifying the task, or lengthening its chain of thought. The gap also appears with an agentic harness on competition math. The abstract concludes that trace training alone cannot transfer the harness's representational gain into weights of a same-class student.

### Source excerpt

arXiv:2610.27157v1 Announce Type: new Abstract: Inference-time harnesses, i.e., external procedures that call a model many times and keep state across calls, can substantially improve model capabilities. In this paper, we investigate the benefits of using such harnesses and the extent to which these benefits can be internalized through training. More precisely, we conceptualize a harness $\mathcal{A}$ as an arbitrary wrapper around a base model $M$ and attempt to distill a student model from the harness's traces. Empirically we find that, even with simple scaffolds and small models, the inference-time harness significantly outperforms a distilled student. We then prove that such a gap is due to a fundamental representational limitation: inference-time harnesses can compute a closure of their per-call primitive that is $\NL$-complete even when the primitive is a trivial $\AC^0$ lookup, and that no fixed-depth forward pass can represent under the widely believed conjecture that $\NL\not\subseteq\TC^0$. Our theory suggests that expanding the representational capacity of the student relative to the task at hand, e.g., by enlarging the model, making the task easier, or lengthening its chain-of-thought, is necessary to close the gap, which we verify empirically. Finally, we scale to an agentic harness on competition math, where the same gap reappears. Overall, our results show that a harness confers on the base model a profound gain in representational power, one that training a same-class student on its traces cannot transfer back into the weights.

## GitHub App improvements for least privilege and management

DevFeed: [GitHub App improvements for least privilege and management](<https://devfeed.tech/articles/github-app-improvements-for-least-privilege-and-management-86461.md>)

Original publisher: [Read original article](<https://github.blog/changelog/2026-10-13-github-app-improvements-for-least-privilege-and-management>)

Author: Allison

Published: 2026-10-13T23:17:55Z

Content type: release

Language: en

Sources: [GitHub Changelog](<https://devfeed.tech/sources/github-changelog.md>)

Topics: [GitHub](<https://devfeed.tech/topics/github.md>), [GitHub API](<https://devfeed.tech/topics/github-api.md>)

Tags: [2](<https://devfeed.tech/tags/2.md>), [3](<https://devfeed.tech/tags/3.md>), [about](<https://devfeed.tech/tags/about.md>), [access-tokens](<https://devfeed.tech/tags/access-tokens.md>), [accessible](<https://devfeed.tech/tags/accessible.md>), [account](<https://devfeed.tech/tags/account.md>), [also](<https://devfeed.tech/tags/also.md>), [app](<https://devfeed.tech/tags/app.md>), [apps](<https://devfeed.tech/tags/apps.md>), [developers](<https://devfeed.tech/tags/developers.md>), [easier](<https://devfeed.tech/tags/easier.md>), [enterprise-management-tools](<https://devfeed.tech/tags/enterprise-management-tools.md>), [fewer](<https://devfeed.tech/tags/fewer.md>), [github](<https://devfeed.tech/tags/github.md>), [github-apps](<https://devfeed.tech/tags/github-apps.md>), [help](<https://devfeed.tech/tags/help.md>), [improvement](<https://devfeed.tech/tags/improvement.md>), [improvements](<https://devfeed.tech/tags/improvements.md>), [least-privilege](<https://devfeed.tech/tags/least-privilege.md>), [management](<https://devfeed.tech/tags/management.md>), [permissions](<https://devfeed.tech/tags/permissions.md>), [platform-governance](<https://devfeed.tech/tags/platform-governance.md>), [request](<https://devfeed.tech/tags/request.md>)

### AI overview

GitHub's updates add finer-grained controls and management options for GitHub Apps. The new `repository_creation:write` permission lets apps and fine-grained PATs create repositories without the broader `repository_administration:write` permission; an app still needs organization administrator installation. `installation_repositories:write` lets callers add or remove a repository from an organization's app installation, subject to user role requirements. Repository administrators can add repositories to apps already approved at the organization level, while organization-level permissions, repository administration, and repository creation still require organization owner approval. Apps also support `offline_access`, which provides a refresh token alongside an expiring access token for that authentication; other sessions and the app's statically selected permissions are unaffected. The Get an app API now supports additional private and internal app detail access where ownership or enterprise relationships qualify. Credentials move to a dedicated tab ahead of planned workload federation features. Most changes are slated for GHES 3.24; `offline_access` is available in GHES 3.23.

### Source excerpt

We're releasing several GitHub Apps improvements today that help developers move off of classic PATs, improve GitHub App management, and request fewer sensitive permissions. We've also made it easier for... The post GitHub App improvements for least privilege and management appeared first on The GitHub Blog.

## Get Ready for Electronic Halloween with Adafruit Matrix Portal

DevFeed: [Get Ready for Electronic Halloween with Adafruit Matrix Portal](<https://devfeed.tech/articles/get-ready-for-electronic-halloween-with-adafruit-matrix-portal-64011.md>)

Original publisher: [Read original article](<https://blog.adafruit.com/2026/10/02/get-ready-for-electronic-halloween-with-adafruit-matrix-portal/>)

Author: Stephanie

Published: 2026-10-02T14:00:49Z

Content type: article

Language: en

Sources: [Adafruit Industries](<https://devfeed.tech/sources/adafruit-industries-makers-hackers-artists-designers-and-engineers.md>)

Topics: [Flip-Disc Display](<https://devfeed.tech/topics/flip-disc-display.md>), [MicroPython](<https://devfeed.tech/topics/micropython.md>)

Tags: [adafruit](<https://devfeed.tech/tags/adafruit.md>), [adafruit-matrix-portal](<https://devfeed.tech/tags/adafruit-matrix-portal.md>), [custom](<https://devfeed.tech/tags/custom.md>), [display](<https://devfeed.tech/tags/display.md>), [easier](<https://devfeed.tech/tags/easier.md>), [electronic](<https://devfeed.tech/tags/electronic.md>), [electronic-halloween](<https://devfeed.tech/tags/electronic-halloween.md>), [halloween](<https://devfeed.tech/tags/halloween.md>), [instant](<https://devfeed.tech/tags/instant.md>), [it-s](<https://devfeed.tech/tags/it-s.md>), [just](<https://devfeed.tech/tags/just.md>), [led](<https://devfeed.tech/tags/led.md>), [like](<https://devfeed.tech/tags/like.md>), [make](<https://devfeed.tech/tags/make.md>), [matrix](<https://devfeed.tech/tags/matrix.md>), [matrix-portal](<https://devfeed.tech/tags/matrix-portal.md>), [play](<https://devfeed.tech/tags/play.md>), [plug](<https://devfeed.tech/tags/plug.md>), [rgb](<https://devfeed.tech/tags/rgb.md>), [up](<https://devfeed.tech/tags/up.md>)

### AI overview

The Adafruit Matrix Portal provides plug-and-play RGB matrix displays for Halloween projects. Users can program projects with CircuitPython and the Protomatter matrix library on SAMD51 hardware.

### Source excerpt

What's the easiest way to make a custom colorful LED display for Halloween? Like, no solder, no wiring, just instant plug-and-play? Adafruit Matrix Portal! With our add-on for RGB Matricies it's never been easier. Plug directly into the back of any Adafruit HUB-75 display (all the ones we stock will work) from 16x32 up to [...]

## Google tag gateway: Now on Webflow

DevFeed: [Google tag gateway: Now on Webflow](<https://devfeed.tech/articles/google-tag-gateway-now-on-webflow-109591.md>)

Original publisher: [Read original article](<https://www.webflow.com/blog/google-tag-gateway-now-on-webflow>)

Author: Webflow Team

Published: 2026-09-16T00:00:00Z

Content type: release

Language: en

Sources: [Webflow Blog](<https://devfeed.tech/sources/webflow-blog.md>)

Topics: [webflow](<https://devfeed.tech/topics/webflow.md>)

Tags: [2](<https://devfeed.tech/tags/2.md>), [2025](<https://devfeed.tech/tags/2025.md>), [build](<https://devfeed.tech/tags/build.md>), [business](<https://devfeed.tech/tags/business.md>), [can](<https://devfeed.tech/tags/can.md>), [development](<https://devfeed.tech/tags/development.md>), [driving](<https://devfeed.tech/tags/driving.md>), [easier](<https://devfeed.tech/tags/easier.md>), [foundation](<https://devfeed.tech/tags/foundation.md>), [gateway](<https://devfeed.tech/tags/gateway.md>), [google](<https://devfeed.tech/tags/google.md>), [help](<https://devfeed.tech/tags/help.md>), [make](<https://devfeed.tech/tags/make.md>), [marketing](<https://devfeed.tech/tags/marketing.md>), [measurement](<https://devfeed.tech/tags/measurement.md>), [results](<https://devfeed.tech/tags/results.md>), [tag](<https://devfeed.tech/tags/tag.md>), [understand](<https://devfeed.tech/tags/understand.md>), [webflow](<https://devfeed.tech/tags/webflow.md>), [with](<https://devfeed.tech/tags/with.md>), [you-re](<https://devfeed.tech/tags/you-re.md>)

### AI overview

Webflow says its native Google tag integration now activates Google tag gateway, routing conversion data through first-party website infrastructure. Existing native users can republish to enable it. For a new setup, add the Google tag ID in Site Settings -> Apps & Integrations, publish, then verify the gateway status is Active. For migration from custom code, save a backup, add the ID to the native integration, remove only the legacy tag-ID scripts (retain event snippets still in use), and test on the staging domain with Tag Assistant before publishing live. The source recommends Advanced Consent Mode with global default-denied settings or managing consent-based firing through Google Tag Manager. It reports average 14% conversion uplift and up to 7% lower CPA among adopters, with individual results varying.

### Source excerpt

When you build on Webflow, you're creating experiences designed to help your business stand out and grow. Underpinning those experiences with a strong measurement foundation can help you understand what's driving results and make more informed marketing decisions. Now, Webflow makes it easier to strengthen your measurement foundation.

## Pinning saved views to the repository issues sidebar is generally available and more

DevFeed: [Pinning saved views to the repository issues sidebar is generally available and more](<https://devfeed.tech/articles/pinning-saved-views-to-the-repository-issues-sidebar-is-generally-available-and-more-71506.md>)

Original publisher: [Read original article](<https://github.blog/changelog/2026-08-20-pin-projects-views-and-milestones-to-the-repository-sidebar>)

Author: Allison

Published: 2026-08-20T23:29:34Z

Content type: release

Language: en

Sources: [GitHub Changelog](<https://devfeed.tech/sources/github-changelog.md>)

Topics: [GitHub Issues](<https://devfeed.tech/topics/github-issues.md>), [Repository](<https://devfeed.tech/topics/repository.md>)

Tags: [api](<https://devfeed.tech/tags/api.md>), [away](<https://devfeed.tech/tags/away.md>), [based](<https://devfeed.tech/tags/based.md>), [blocking](<https://devfeed.tech/tags/blocking.md>), [can](<https://devfeed.tech/tags/can.md>), [community](<https://devfeed.tech/tags/community.md>), [dashboard](<https://devfeed.tech/tags/dashboard.md>), [dependency](<https://devfeed.tech/tags/dependency.md>), [discussion](<https://devfeed.tech/tags/discussion.md>), [easier](<https://devfeed.tech/tags/easier.md>), [filter](<https://devfeed.tech/tags/filter.md>), [focus](<https://devfeed.tech/tags/focus.md>), [github](<https://devfeed.tech/tags/github.md>), [issue](<https://devfeed.tech/tags/issue.md>), [join](<https://devfeed.tech/tags/join.md>), [just](<https://devfeed.tech/tags/just.md>), [making](<https://devfeed.tech/tags/making.md>), [one](<https://devfeed.tech/tags/one.md>), [pin](<https://devfeed.tech/tags/pin.md>), [repository](<https://devfeed.tech/tags/repository.md>)

### AI overview

GitHub now lets users pin saved views in the repository issues sidebar, including when it is collapsed. The update also adds reaction profile avatars, an Issues Dashboard density control to show more issues at once, and an option to hide closed sub-issues. Issue dependency REST API endpoints (blocked_by, blocking, and relates_to) now filter results according to the token's granted scope.

### Source excerpt

Pinning saved views to the repository issues sidebar You can now pin saved views⁠ to the repository issues sidebar, making the views you use most just one click away, even...

## New Report: AI threats are here. Why Q2 2026 signals the end of traditional patch cycles

DevFeed: [New Report: AI threats are here. Why Q2 2026 signals the end of traditional patch cycles](<https://devfeed.tech/articles/new-report-ai-threats-are-here-why-q2-2026-signals-the-end-of-traditional-patch-cycles-50727.md>)

Original publisher: [Read original article](<https://www.rapid7.com/blog/post/tr-new-report-ai-threats-q2-2026-ends-traditional-patch-cycles>)

Author: Rapid7 Labs

Published: 2026-08-18T12:49:46Z

Content type: article

Language: en

Sources: [Rapid7 Cybersecurity](<https://devfeed.tech/sources/rapid7-cybersecurity-blog.md>)

Topics: [Cybercrime](<https://devfeed.tech/topics/cybercrime.md>), [known exploitable vulnerabilities](<https://devfeed.tech/topics/known-exploitable-vulnerabilities.md>), [alert triage](<https://devfeed.tech/topics/alert-triage.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-assisted](<https://devfeed.tech/tags/ai-assisted.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [authentication](<https://devfeed.tech/tags/authentication.md>), [automation](<https://devfeed.tech/tags/automation.md>), [cves](<https://devfeed.tech/tags/cves.md>), [cvss](<https://devfeed.tech/tags/cvss.md>), [cwe](<https://devfeed.tech/tags/cwe.md>), [cybersecurity-company](<https://devfeed.tech/tags/cybersecurity-company.md>), [cycles](<https://devfeed.tech/tags/cycles.md>), [disclosure](<https://devfeed.tech/tags/disclosure.md>), [easier](<https://devfeed.tech/tags/easier.md>), [exploit](<https://devfeed.tech/tags/exploit.md>), [exploitability](<https://devfeed.tech/tags/exploitability.md>), [exploitation](<https://devfeed.tech/tags/exploitation.md>), [exploited](<https://devfeed.tech/tags/exploited.md>), [exposure-assessment-platform](<https://devfeed.tech/tags/exposure-assessment-platform.md>), [exposure-management](<https://devfeed.tech/tags/exposure-management.md>), [initial-access](<https://devfeed.tech/tags/initial-access.md>), [interaction](<https://devfeed.tech/tags/interaction.md>), [managed-detection-and-response](<https://devfeed.tech/tags/managed-detection-and-response.md>), [managed-security-solutions](<https://devfeed.tech/tags/managed-security-solutions.md>), [patch](<https://devfeed.tech/tags/patch.md>), [ransomware](<https://devfeed.tech/tags/ransomware.md>), [report](<https://devfeed.tech/tags/report.md>), [research](<https://devfeed.tech/tags/research.md>), [threat-research](<https://devfeed.tech/tags/threat-research.md>), [threats](<https://devfeed.tech/tags/threats.md>), [vulnerability-management](<https://devfeed.tech/tags/vulnerability-management.md>)

### AI overview

Rapid7 Labs reports that Q2 2026 brought 8,539 high- and critical-severity CVE disclosures--about twice Q2 2025's total--while newly exploited vulnerabilities stayed roughly steady at 40. The excerpt argues that disclosure volume and faster exploitation make patching by severity or volume alone inadequate; security teams should prioritize exposures attackers can actually reach. It reports that 62% of exploited vulnerabilities required no user interaction, up from 53% a year earlier, and missing-authentication flaw disclosures rose 247%. The excerpt also notes persistent Iranian, North Korean, and Russian APT activity across several sectors, and says Qilin led ransomware with 263 listed victims. Rapid7's Incident Response team saw ClickFix, fake CAPTCHA, and trusted-platform social engineering in 31.8% of its incidents. Detailed prioritization steps and the report's full findings are not included in the excerpt.

### Source excerpt

You can't patch everything. So what do you fix first? Findings in Q2 2026 have changed traditional answers. The latest Quarterly Threat Landscape Report from Rapid7 Labs shows vulnerability disclosures still surging while attackers use automation and AI-assisted tooling to compress the time between disclosure and exploitation. The gap that patch cycles were built to fill is closing. Speed and volume are overwhelming security teams that have relied on traditional patch cycles and reactive programs. Success going forward can't be about patching as much as possible - it has to be about understanding what matters most and reducing the exposures attackers can actually reach. Here are the four trends that defined Q2 2026, and what they mean for your security program as you define priorities for Q3 and beyond: The volume of disclosures hit another milestone There were 8,539 new high- and critical-severity CVEs (CVSS 7.0-10.0) this quarter- double the number reported in the same quarter last year (4,268). Meanwhile, the number of newly exploited vulnerabilities held roughly steady (40). The takeaway isn't that exploitation exploded - it's that disclosure volume is far outstripping what any team can triage. The report breaks down which of those disclosures are actually reachable and how to triage by exploitability instead of severity score alone.Initial access keeps getting easier Nearly two-thirds of exploited vulnerabilities this quarter (62%) required no user interaction - no stolen credentials, no phishing victim, no click. Attackers reach and exploit them on their own, and that share is up nine points year over year (from 53% in Q2 2025). Reinforcing the trend, disclosures of missing-authentication flaws (CWE-306) surged 247% year over year - a fast-expanding pool of internet-facing systems that require no login at all. This is the quarter's clearest signal - and the report details exactly which exposures to close first, and how, before the exploitation curve catches up

## Automatic repository linking: cross-repo context without manual setup

DevFeed: [Automatic repository linking: cross-repo context without manual setup](<https://devfeed.tech/articles/automatic-repository-linking-cross-repo-context-without-manual-setup-79607.md>)

Original publisher: [Read original article](<https://coderabbit.ai/blog/automatic-repository-linking-cross-repo-context>)

Author: Erfan Al-Hossami

Published: 2026-06-11T00:00:00Z

Content type: release

Language: en

Sources: [CodeRabbit](<https://devfeed.tech/sources/coderabbit.md>)

Topics: [Code review](<https://devfeed.tech/topics/code-review.md>), [Pull Request](<https://devfeed.tech/topics/pull-request.md>), [Repository](<https://devfeed.tech/topics/repository.md>), [context](<https://devfeed.tech/topics/context.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agents](<https://devfeed.tech/tags/agents.md>), [also](<https://devfeed.tech/tags/also.md>), [analysis](<https://devfeed.tech/tags/analysis.md>), [api](<https://devfeed.tech/tags/api.md>), [approach](<https://devfeed.tech/tags/approach.md>), [automatic](<https://devfeed.tech/tags/automatic.md>), [can](<https://devfeed.tech/tags/can.md>), [context](<https://devfeed.tech/tags/context.md>), [cross](<https://devfeed.tech/tags/cross.md>), [easier](<https://devfeed.tech/tags/easier.md>), [english](<https://devfeed.tech/tags/english.md>), [impact](<https://devfeed.tech/tags/impact.md>), [linking](<https://devfeed.tech/tags/linking.md>), [merge](<https://devfeed.tech/tags/merge.md>), [organization](<https://devfeed.tech/tags/organization.md>), [repo](<https://devfeed.tech/tags/repo.md>), [repositories](<https://devfeed.tech/tags/repositories.md>), [repository](<https://devfeed.tech/tags/repository.md>), [review](<https://devfeed.tech/tags/review.md>), [setup](<https://devfeed.tech/tags/setup.md>)

### AI overview

Automatic Repository Linking discovers cross-repository dependencies from code signals--including imports, dependency manifests, API usage, contracts, shared patterns, and README files--and adds linked repositories as context for CodeRabbit reviews. Its research agent can inspect those repositories and report potential downstream impact with file and line references; the Review info section shows which repositories were considered. Automatic links are stored separately and do not overwrite manual links. The feature is available to Pro plus and Enterprise customers through knowledge base settings. If an organization opts out of knowledge base features, both Automatic Repository Linking and Multi-Repo Analysis are disabled. The text contrasts this code-based discovery with manual scoping and approaches based on contributor activity, which it says does not establish a technical dependency.

### Source excerpt

CodeRabbit can detect related repositories across your organization and use them as review context, so cross-repo impact is easier to catch before you merge.

## Agentic Nx Import: Let an Agent Drive Your Monorepo Migrations

DevFeed: [Agentic Nx Import: Let an Agent Drive Your Monorepo Migrations](<https://devfeed.tech/articles/agentic-nx-import-let-an-agent-drive-your-monorepo-migrations-21396.md>)

Original publisher: [Read original article](<https://nx.dev/blog/agentic-nx-import>)

Author: Juri Strumpflohner

Published: 2026-04-30T00:00:00Z

Content type: article

Language: en

Sources: [Juri Strumpflohner](<https://devfeed.tech/sources/juri-strumpflohner.md>)

Topics: [monorepo](<https://devfeed.tech/topics/monorepo.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [agentic-coding](<https://devfeed.tech/topics/agentic-coding.md>), [Developer Tools](<https://devfeed.tech/topics/developer-tools.md>), [Agent Skills](<https://devfeed.tech/topics/agent-skills.md>), [Claude Code](<https://devfeed.tech/topics/claude-code.md>), [cursor](<https://devfeed.tech/topics/cursor.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agent-harnesses](<https://devfeed.tech/tags/agent-harnesses.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [blog](<https://devfeed.tech/tags/blog.md>), [cli](<https://devfeed.tech/tags/cli.md>), [codex](<https://devfeed.tech/tags/codex.md>), [easier](<https://devfeed.tech/tags/easier.md>), [files](<https://devfeed.tech/tags/files.md>), [flexibility](<https://devfeed.tech/tags/flexibility.md>), [git](<https://devfeed.tech/tags/git.md>), [gradle](<https://devfeed.tech/tags/gradle.md>), [graph](<https://devfeed.tech/tags/graph.md>), [history](<https://devfeed.tech/tags/history.md>), [import](<https://devfeed.tech/tags/import.md>), [migrations](<https://devfeed.tech/tags/migrations.md>), [monorepo](<https://devfeed.tech/tags/monorepo.md>), [nx](<https://devfeed.tech/tags/nx.md>), [projects](<https://devfeed.tech/tags/projects.md>), [video](<https://devfeed.tech/tags/video.md>)

### AI overview

Nx's agentic import workflow leaves predictable migration work to the Nx CLI and uses an AI agent to handle workspace-specific gaps. The CLI clones projects, moves files while preserving Git history through subtree merges, and detects and configures the relevant Nx plugin; the agent invokes it rather than reimplementing those steps. This reduces the need for the agent to inspect every source file, while allowing it to respond to issues such as missing runtimes, follow-up file moves, and build failures. The excerpt recommends installing Nx skills for the agent harness, prompting the agent to use the skill, and verifying imports by building the project graph and running builds. The provided excerpt cuts off during the verification instructions.

### Source excerpt

AI agents make importing existing projects into an Nx monorepo easier: does the heavy lifting while the agent handles workspace-specific gaps.

## Code scanning: Batch apply security alert suggestions on pull requests

DevFeed: [Code scanning: Batch apply security alert suggestions on pull requests](<https://devfeed.tech/articles/code-scanning-batch-apply-security-alert-suggestions-on-pull-requests-71887.md>)

Original publisher: [Read original article](<https://github.blog/changelog/2026-04-07-code-scanning-batch-apply-security-alert-suggestions-on-pull-requests>)

Author: Allison

Published: 2026-04-07T10:22:33Z

Content type: release

Language: en

Sources: [GitHub Changelog](<https://devfeed.tech/sources/github-changelog.md>)

Topics: [code security](<https://devfeed.tech/topics/code-security.md>), [GitHub](<https://devfeed.tech/topics/github.md>)

Tags: [actions](<https://devfeed.tech/tags/actions.md>), [alert](<https://devfeed.tech/tags/alert.md>), [alerts](<https://devfeed.tech/tags/alerts.md>), [apply](<https://devfeed.tech/tags/apply.md>), [batch](<https://devfeed.tech/tags/batch.md>), [batching](<https://devfeed.tech/tags/batching.md>), [can](<https://devfeed.tech/tags/can.md>), [changes](<https://devfeed.tech/tags/changes.md>), [code](<https://devfeed.tech/tags/code.md>), [code-scanning](<https://devfeed.tech/tags/code-scanning.md>), [commit](<https://devfeed.tech/tags/commit.md>), [documentation](<https://devfeed.tech/tags/documentation.md>), [easier](<https://devfeed.tech/tags/easier.md>), [faster](<https://devfeed.tech/tags/faster.md>), [files](<https://devfeed.tech/tags/files.md>), [github](<https://devfeed.tech/tags/github.md>), [github-com](<https://devfeed.tech/tags/github-com.md>), [pull-requests](<https://devfeed.tech/tags/pull-requests.md>), [scanning](<https://devfeed.tech/tags/scanning.md>), [with](<https://devfeed.tech/tags/with.md>)

### AI overview

GitHub code scanning alerts on pull requests can now be fixed in batches from the Files changed tab: add suggested fixes to a batch and apply them in a single commit. GitHub says this runs one scan instead of a separate scan for each alert, reducing remediation and review time. The feature is generally available on github.com.

### Source excerpt

GitHub code scanning alerts on pull requests are now easier to address with bulk actions. You can now apply fixes for code scanning alerts in the Files changed tab by...

## GitHub Mobile: Stay in flow with a refreshed Copilot tab and native session logs

DevFeed: [GitHub Mobile: Stay in flow with a refreshed Copilot tab and native session logs](<https://devfeed.tech/articles/github-mobile-stay-in-flow-with-a-refreshed-copilot-tab-and-native-session-logs-71904.md>)

Original publisher: [Read original article](<https://github.blog/changelog/2026-04-01-github-mobile-stay-in-flow-with-a-refreshed-copilot-tab-and-native-session-logs>)

Author: Allison

Published: 2026-04-01T16:36:10Z

Content type: release

Language: en

Sources: [GitHub Changelog](<https://devfeed.tech/sources/github-changelog.md>)

Topics: [GitHub](<https://devfeed.tech/topics/github.md>), [GitHub Copilot](<https://devfeed.tech/topics/github-copilot.md>), [Android](<https://devfeed.tech/topics/android.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-workflows](<https://devfeed.tech/tags/agentic-workflows.md>), [also](<https://devfeed.tech/tags/also.md>), [android](<https://devfeed.tech/tags/android.md>), [app](<https://devfeed.tech/tags/app.md>), [build](<https://devfeed.tech/tags/build.md>), [can](<https://devfeed.tech/tags/can.md>), [chat](<https://devfeed.tech/tags/chat.md>), [copilot](<https://devfeed.tech/tags/copilot.md>), [easier](<https://devfeed.tech/tags/easier.md>), [github](<https://devfeed.tech/tags/github.md>), [github-mobile](<https://devfeed.tech/tags/github-mobile.md>), [logs](<https://devfeed.tech/tags/logs.md>), [manage](<https://devfeed.tech/tags/manage.md>), [mobile](<https://devfeed.tech/tags/mobile.md>), [native](<https://devfeed.tech/tags/native.md>), [sessions](<https://devfeed.tech/tags/sessions.md>), [with](<https://devfeed.tech/tags/with.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

GitHub Mobile's refreshed Copilot tab adds a Home view for finding agent sessions and chat history, plus a session list with state filters and real-time updates. In the app, users can view full session logs, create and review a pull request from a completed session, and stop a running session. The excerpt says the updates are available in the latest production build on iOS and Android; it specifies the navigation-bar move to the Copilot tab for Android.

### Source excerpt

GitHub Mobile now makes it easier to manage agentic workflows on the go with a refreshed Copilot tab, native session logs, and more in-app controls for agent sessions. On Android,...

## Enhancing Security with User-Specific Access Keys for DigitalOcean Functions

DevFeed: [Enhancing Security with User-Specific Access Keys for DigitalOcean Functions](<https://devfeed.tech/articles/enhancing-security-with-user-specific-access-keys-for-digitalocean-functions-19880.md>)

Original publisher: [Read original article](<https://www.digitalocean.com/blog/functions-user-specific-access-keys>)

Author: Amulya Tomer

Published: 2026-03-23T19:30:06Z

Content type: release

Language: en

Sources: [DigitalOcean](<https://devfeed.tech/sources/digitalocean.md>)

Topics: [Security](<https://devfeed.tech/topics/security.md>), [identity and access management](<https://devfeed.tech/topics/identity-and-access-management.md>), [Access Control](<https://devfeed.tech/topics/access-control.md>), [Secrets Management](<https://devfeed.tech/topics/secrets-management.md>), [personal access token](<https://devfeed.tech/topics/personal-access-token.md>), [rbac](<https://devfeed.tech/topics/rbac.md>)

Tags: [access-control](<https://devfeed.tech/tags/access-control.md>), [access-management](<https://devfeed.tech/tags/access-management.md>), [accountability](<https://devfeed.tech/tags/accountability.md>), [attack](<https://devfeed.tech/tags/attack.md>), [attack-surface](<https://devfeed.tech/tags/attack-surface.md>), [auditability](<https://devfeed.tech/tags/auditability.md>), [automated](<https://devfeed.tech/tags/automated.md>), [automatically](<https://devfeed.tech/tags/automatically.md>), [challenges](<https://devfeed.tech/tags/challenges.md>), [code](<https://devfeed.tech/tags/code.md>), [credentials](<https://devfeed.tech/tags/credentials.md>), [developer](<https://devfeed.tech/tags/developer.md>), [digitalocean](<https://devfeed.tech/tags/digitalocean.md>), [disruption](<https://devfeed.tech/tags/disruption.md>), [easier](<https://devfeed.tech/tags/easier.md>), [ensures](<https://devfeed.tech/tags/ensures.md>), [functions](<https://devfeed.tech/tags/functions.md>), [keys](<https://devfeed.tech/tags/keys.md>), [security](<https://devfeed.tech/tags/security.md>), [trust-security](<https://devfeed.tech/tags/trust-security.md>), [user](<https://devfeed.tech/tags/user.md>)

### AI overview

DigitalOcean Functions is moving from one shared namespace credential to access keys tied to individual users. The excerpt says keys are automatically revoked when a user leaves the team, avoiding manual rotation that could disrupt other developers and production workloads. Users can create multiple keys per namespace for rotation or environment-specific use; user-specific keys also improve accountability and auditability. Keys can optionally expire after a configured TTL, after which they fail to authenticate. The excerpt ends during this expiration description.

### Source excerpt

As teams grow and scale their serverless workloads, managing security postures becomes just as critical as managing code. Our goal at DigitalOcean is to support your growth at every stage. One way we support you is by iterating on our security architecture. Historically, DigitalOcean Functions used a shared credential model within a namespace that is configured in the settings tab of the function view. Same <Token> is shared among all users for a functions namespace While simple to start, this model presented challenges for growing teams: if a team member left or changed roles, the shared credentials remained valid. To secure the namespace, admins had to manually revoke and regenerate keys, disrupting workflows for every other developer and production workload using that shared key. Today, we are excited to announce a considerable upgrade to our access model: user-specific namespace access keys. This update shifts access control from the namespace level to the individual identity level, ensuring that access is granted to specific users rather than through a shared key. How user-specific access keys enhance security This transition to user-specific keys solves several critical use cases for teams: Automated access management: When a team member is removed from your DigitalOcean team, their specific access keys are automatically revoked by the platform. This removes the need for manual key rotation and ensures zero disruption to remaining team members. Multiple keys per namespace: A user can create multiple access keys for a namespace, this would allow for easier manual rotation and management of environment-specific keys. Streamlined accountability: Because actions are now associated with unique user-specific keys, you gain better visibility and auditability into resource management. Expiration support: To limit the attack surface further, access keys can optionally have expiration (TTL). The access key will fail to authenticate for any operation(s) after the expirat

[Next page](<https://devfeed.tech/tags/easier.md?cursor=WyIyMDI2LTAzLTIzVDE5OjMwOjA2KzAwOjAwIiwgImJiNjYzMDk1LTg3NDUtNGE0Zi04NWYzLTBjOTlmZGMzYjk1ZSJd>)