# e2e Published articles for e2e. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Stochastic Autoregressive Learning DevFeed: [Stochastic Autoregressive Learning]() Original publisher: [Read original article]() Author: Ilan Doron-Arad, Idan Mehalel, Elchanan Mossel Published: 2026-11-09T05:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Machine learning](), [Language models](), [Large Language Model]() Tags: [2](), [2025](), [abstract](), [analogue](), [arxiv](), [autoregressive](), [base](), [bernoulli](), [bounded](), [can](), [chain-of-thought](), [class](), [comparison](), [cs-lg](), [d](), [dependencies](), [dimension](), [distribution](), [distributions](), [e2e](), [learning]() ### AI overview The paper studies PAC learning for a generator that repeatedly samples Bernoulli next tokens from an expanding prompt over M steps. It compares base supervision, which provides one-step samples, CoT supervision, which reveals full trajectories, and end-to-end supervision, which reveals only each trajectory's final token; learning is measured under KL divergence. At the same accuracy scale, these tasks have no universal sample-complexity ordering: both CoT relative to base and end-to-end relative to CoT can exceed M/ε by arbitrarily large factors. With adjusted scales, CoT learning at ε is bounded by base learning at ε/M², while end-to-end learning is bounded up to logarithmic factors by (M/ε) times CoT sample complexity at Θ(ε). The abstract says these dependencies and scales are essentially tight and notes a study of d-dimensional logistic functions. ### Source excerpt arXiv:2608.07224v2 Announce Type: replace Abstract: Motivated by LLMs, which generate outputs by iteratively sampling from next-token distributions, we introduce a PAC-learning model for binary stochastic autoregressive learning. This generalizes the deterministic autoregressive learning framework of Joshi et al., COLT 2025. In our model, one fixed generator assigns a Bernoulli next-token distribution to every prompt string. Starting from an input prompt, a token is sampled and appended to the prompt; the same generator is then applied again to this expanded prompt; this procedure is repeated for $M$ steps. Three forms of supervision are considered: base one-step samples, chain-of-thought (CoT) samples that reveal full random trajectories of length $M$, and end-to-end (e2e) samples that reveal only the final token of length $M$ trajectories. For a generator class, we study the minimum number of samples $m_{base}(\varepsilon),m_{CoT}(\varepsilon), m_{e2e}(\varepsilon)$, resp., required to learn the one-step probabilities in the base model, and the final-token probability in the CoT and e2e models, under KL divergence. We show that stochastic autoregressive learning fundamentally differs from the deterministic theory. At scale $\varepsilon$, there is no universal comparison between the three learning tasks: both $m_{CoT}/m_{base}$ and $m_{e2e}/m_{CoT}$ can be made simultaneously arbitrarily larger than $M/\varepsilon$, the natural analogue for the existing deterministic results. Nevertheless, after altering scales, for every class, CoT learning at scale $\varepsilon$ is upper-bounded by base learning at scale $\varepsilon/M^2$, whereas e2e learning at scale $\varepsilon$ is upper-bounded, up to logarithmic factors, by $(M/\varepsilon) m_{CoT}(\Theta(\varepsilon))$. These dependencies and scales are essentially tight. We complement these bounds by studying dimension $d$ logistic functions in our model. ## FILO: A Unified Fisher-Geometric Framework for Low-Rank Adaptation DevFeed: [FILO: A Unified Fisher-Geometric Framework for Low-Rank Adaptation]() Original publisher: [Read original article]() Author: Ziyun Liu, Fengmiao Bian, Jian-Feng Cai Published: 2026-11-06T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence](), [arXiv: Machine Learning]() Topics: [lora]() Tags: [adaptive](), [arc](), [cs-ai](), [cs-cl](), [cs-lg](), [diffusion](), [e2e](), [factor](), [framework](), [gpt](), [gpu](), [gpu-memory](), [gradient](), [lora](), [matrix](), [mistral](), [momentum](), [solve](), [space](), [weight]() ### AI overview LoRA's factorization makes the induced factor-space preconditioner singular, preventing a unique inversion of a preconditioned weight-space direction. FILO frames low-rank adaptation around the choice of weight-space preconditioner and factor-space inversion strategy, grouping existing methods into four families. Its FILO-Adafactor instance uses Adafactor's diagonal Kronecker preconditioner and selects a factor update that minimizes weighted imbalance; the resulting weight update is the closest LoRA approximation under the preconditioner-weighted norm. The abstract reports competitive or improved results versus representative LoRA optimizers on language-model tasks and diffusion personalization, with peak GPU memory at the LoRA optimizer level. These claims reflect the abstract, not full-paper details. ### Source excerpt arXiv:2605.08734v2 Announce Type: replace-cross Abstract: Low-Rank Adaptation (LoRA) reparameterizes a weight matrix $W$ as a product of two low-rank factors, but the Jacobian $J_G$ of the generator mapping the factors to the weight matrix is rank-deficient, so the factor-space preconditioner $J_G^* F_t J_G$ induced by any $W$-space preconditioner $F_t$ is singular, and consequently the standard chain rule cannot be uniquely inverted to map a preconditioned $W$-space direction back to a factor-space update. Therefore, we introduce FILO, a unified Fisher-geometric framework for low-rank adaptation that enables factor updates without directly inverting this singular operator and casts existing LoRA optimizers by their factor-space inversion strategy and $W$-space preconditioner $F_t$. Existing methods occupy four families along these axes: factor-space adaptive updates, block-diagonal surrogates for $J_G^* J_G$, Frobenius-residual pseudoinverse methods, and Riemannian manifold methods. Within this design space, a gradient-statistics-aware $F_t$ paired with a closed-form factor-space solve at $O((m+n)r)$ memory remains underexplored. We instantiate FILO as FILO-Adafactor by adopting the Adafactor diagonal Kronecker preconditioner $H_t$ on $W$ and selecting from the resulting factor-space solution family the element minimizing an $H_t$-weighted imbalance between the two factor contributions. By construction, the induced weight update is the closest LoRA approximation to the preconditioned $W$-space direction under the $H_t$-weighted norm. Across GPT-2 on E2E, Mistral-7B and Qwen2-7B on GLUE, ARC, and GSM8K, and diffusion-model personalization, FILO-Adafactor and its momentum variant are competitive with or improve over a representative set of LoRA optimizers while keeping peak GPU memory at the LoRA optimizer level. ## Re-examining low-rank adaptation for private LLM fine-tuning DevFeed: [Re-examining low-rank adaptation for private LLM fine-tuning]() Original publisher: [Read original article]() Author: Ali Dadsetan, Frank Rudzicz Published: 2026-11-04T05:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [lora](), [Fine-tuning](), [Large Language Model](), [LLM optimization](), [LLM evaluation / benchmarking](), [qwen]() Tags: [benchmarks](), [classification](), [code](), [convergence](), [cs-lg](), [dart](), [data](), [e2e](), [experiments](), [exploit](), [fine-tuning](), [generation](), [gradient](), [guarantees](), [language](), [language-models](), [large-language-models-llms](), [llm](), [llm-fine-tuning](), [private](), [rank]() ### AI overview The abstract reports that DP-SGD's isotropic noise inflates gradient singular values, disrupting their naturally fast decay and potentially slowing private fine-tuning. In experiments on GLUE with RoBERTa and E2E/DART table-to-text generation with Qwen and Llama models up to 4B parameters, partially restoring the original singular-value profile significantly accelerates convergence without compromising privacy guarantees. The work is motivated by reaching similar performance with less computation when benchmark performance at a fixed privacy level has largely saturated. ### Source excerpt arXiv:2510.01137v4 Announce Type: replace Abstract: Privacy is a central concern when fine-tuning large language models (LLMs) on sensitive data, and differentially private stochastic gradient descent (DP-SGD) provides formal privacy guarantees. When performance at a fixed privacy level largely saturates on established benchmarks such as GLUE, we ask whether the same performance can be reached with less computation. Both theory and practice show that lower-rank models are better suited to DP training, a property especially relevant for LLMs, whose fine-tuning gradients exhibit a strong low-rank structure. Methods such as DP-LoRA exploit this by expressing each layer's weight update as a sum of only a few non-zero rank-one components. However, we argue that while having few non-zero components is important, the isotropic noise injected by DP-SGD inflates the singular values of the gradient matrix, disrupting their naturally fast decay. We investigate whether this noise-induced spectral distortion slows optimization. Experiments on language classification (GLUE with RoBERTa) and table-to-text generation (E2E and DART with Qwen and Llama models up to 4B parameters) show that partially restoring the original singular-value profile significantly accelerates DP-SGD convergence without compromising privacy guarantees. Our code is available at https://github.com/alidadsetan/re-exam-private-low-rank. ## Mechanistic Interpretability for End-to-End Self-Driving DevFeed: [Mechanistic Interpretability for End-to-End Self-Driving]() Original publisher: [Read original article]() Author: Manav Gagvani, Benjamin Namikas, Sivamurugan Velmurugan, Nysa Kumar, Shrey Sharma, Thomas Peterson, Pratyush Mathur, Hyunseo Chang, Rohan Paleja Published: 2026-11-02T05:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [autonomous vehicles](), [AI agents in autonomous vehicles](), [Tensorflow]() Tags: [abstract](), [adapter](), [application](), [apply](), [arxiv](), [autoencoders](), [autonomous](), [autonomous-driving](), [behavior](), [build](), [but](), [camera](), [changes](), [clustering](), [code](), [cs-cv](), [cs-lg](), [cs-ro](), [driving](), [e2e](), [interpretability](), [mechanistic-interpretability](), [self-driving]() ### AI overview The study applies Sparse Autoencoders (SAEs) to activations from a single-camera end-to-end path-planning model. Clustering SAE latents recovered route intent with 98.3% accuracy without supervision, and counterfactual image edits showed features responding to traffic light state and stop sign presence. Intervening on individual latents caused predictable, monotonic changes in lateral displacement, curvature, and braking. A lightweight adapter using SAE latents reduced trajectory selection error by 3.44% and outperformed an equivalent adapter without them. These results suggest the sparse representations expose behaviorally useful structure in this model; the supplied abstract does not establish performance beyond the studied setup. ### Source excerpt arXiv:2610.41185v1 Announce Type: cross Abstract: End-to-end (E2E) autonomous driving models achieve strong planning performance but remain largely opaque, limiting observability and trust. We apply mechanistic interpretability to E2E driving by training Sparse Autoencoders (SAEs) on the internal activations of a simple, single-camera end-to-end path planning model. Unsupervised clustering of SAE latents reveals interpretable structure aligned with route intent, vehicle speed, and braking behavior, recovering route intent with 98.3% accuracy without supervision. Through paired counterfactual image edits, we further show that specific SAE features are visually grounded and systematically respond to scene changes such as traffic light state and stop sign presence. Steering individual SAE latents produces predictable, monotonic changes in planner outputs including lateral displacement, curvature, and braking. Finally, we demonstrate a practical application of these representations: we build a lightweight adapter trained on SAE latents that reduces trajectory selection error by 3.44%, outperforming an equivalent adapter that omits SAE latents, confirming that the sparse representation captures useful information for interpretability. Code and visualizations are available at https://mgagvani.github.io/mechanistic-interpretability-for-e2e-driving/ ## How to Build Bug-Free Applications with Claude Code DevFeed: [How to Build Bug-Free Applications with Claude Code]() Original publisher: [Read original article]() Author: Eivind Kjosbakken Published: 2026-10-31T16:00:00Z Content type: article Language: en Sources: [Towards Data Science]() Topics: [Test-driven development](), [Claude Code](), [CI/CD]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [ai-coding-assistants](), [applications](), [browser](), [bugs](), [ci-cd](), [ci-cd-pipeline](), [claude-code](), [codex](), [e2e](), [llm](), [llm-applications](), [testing](), [tests]() ### AI overview The article recommends agent-driven end-to-end tests to catch application bugs through user-like journeys before users encounter them. Give an agent browser access, for example through Playwright MCP, and prompt it to navigate to a page, perform an action, and check the expected result. Compared with manually scripted tests, the author says these agents are quicker to create, easier to maintain when page designs change, and can support broader coverage and different user roles. Run the suite before merging through CI/CD and on a recurring schedule in development or production to catch issues such as environment changes. Recurring tests need a reliable runner, such as a cloud machine, GitHub Actions runner, or Mac mini. The author favors Playwright MCP but notes they have not evaluated other frameworks. ### Source excerpt Use E2E tests to minimize bugs The post How to Build Bug-Free Applications with Claude Code appeared first on Towards Data Science. ## Can Event Detectors Leave the Broadcast Camera? Evidence from Tennis DevFeed: [Can Event Detectors Leave the Broadcast Camera? Evidence from Tennis]() Original publisher: [Read original article]() Author: David Soeiro-Vuong, Andre-Louis Rochet Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Computer vision](), [Machine learning](), [benchmark overfitting machine learning]() Tags: [annotations](), [arbitration](), [benchmark](), [bounce](), [broadcast](), [computer-vision](), [court](), [cs-ai](), [cs-cv](), [e2e](), [hit](), [phone](), [release](), [split](), [timing](), [video](), [vision-language-models]() ### AI overview The benchmark suggests tennis event detectors transfer poorly from broadcast footage to other viewpoints, though approaches differ. It contains 345 frame-level bounce and hit annotations across eight broadcast, elevated, phone and court-level clips, with frozen splits and predictions. Supervised spotters using broadcast weights perform near perfectly on broadcast footage but poorly on phone and court-level video (E2E-Spot pooled bounce F1: 0.130). RallyArb, which combines ball tracking and player poses without fitting to event labels, exceeds 0.8 bounce F1 across three phone and court-level clips, but two were used for tuning; it finds 8 of 11 bounces on the held-out clip and misses human hit timing. Zero-shot vision-language models detect events across viewpoints but miss contact frames by about 0.1 seconds. Leave-one-clip-out fine-tuning reaches about 0.8 bounce F1 on two clips but only 0.2 on a grazing phone angle. ### Source excerpt arXiv:2610.38042v1 Announce Type: cross Abstract: Ball bounces and racket hits are the events from which tennis statistics are derived, and on monocular video the two are easy to confuse. We release a benchmark of 345 bounce and hit annotations at frame level on eight clips that mix broadcast, fixed elevated, phone and court-level viewpoints, with a frozen development and test split, a confusion-aware matcher, clip-level bootstrap intervals and a blind second annotation of three clips. On it we score supervised precise spotters with published broadcast weights, two zero-shot vision-language models, and RallyArb, an arbitration layer over a ball track and player poses that fits no parameter on event labels. The supervised spotters score near-perfectly on broadcast footage and almost nothing on phone and court-level footage, where the pooled bounce F1 of E2E-Spot is 0.130. RallyArb keeps a bounce F1 above 0.8 on the three phone and court-level clips, two of which were used to tune it, and finds 8 of 11 bounces on the held-out one, but it places hits too far from the contact frame to match human timing. The vision-language models detect events on every viewpoint and miss the contact frame by about 0.1 s. Fine-tuned leave-one-clip-out from its broadcast weights, the spotter reaches about 0.8 bounce F1 on two phone and court-level clips and stays at 0.2 on a grazing phone angle. The annotations, the evaluator and the frozen predictions of every scored row are released. ## From Research to Practice: An Interactive Rapid Review of Autonomous Driving System Testing in Industry DevFeed: [From Research to Practice: An Interactive Rapid Review of Autonomous Driving System Testing in Industry]() Original publisher: [Read original article]() Author: Qunying Song, Ali Nouri, H{\\aa}kan Sivencrona, Federica Sarro Published: 2026-10-26T04:00:00Z Content type: article Language: en Sources: [arXiv: Software Engineering]() Topics: [autonomous vehicles](), [Rapid Software Testing]() Tags: [autonomous-driving](), [autonomous-driving-system](), [challenges](), [company](), [completeness](), [complex](), [cs-se](), [e2e](), [evaluation](), [industrial](), [industry](), [insights](), [interactive](), [practical](), [practitioner](), [rapid](), [research](), [review](), [study](), [testing]() ### AI overview An interactive rapid review with 21 practitioners from a leading automotive company identified 12 challenges in autonomous driving system testing and prioritized testing approaches and completeness for End-to-End systems. The authors examined 17 relevant studies, most focused on generating critical test scenarios, and assessed their practical relevance. The abstract reports a gap between research and industrial applicability and calls for more context-aware, industry-relevant solutions. It does not detail the individual challenges or the study-by-study assessment. ### Source excerpt arXiv:2605.00531v2 Announce Type: replace Abstract: Autonomous driving systems (ADS) are increasingly deployed in real traffic, yet testing remains fundamentally challenging due to open environments, complex scenarios, and the lack of established processes and acceptance criteria. Despite extensive research, a gap persists between academic advances and their applicability in industrial practice. To address this, we conduct an interactive rapid review in collaboration with 21 practitioners from a leading automotive company. Practitioners identified 12 key challenges in ADS testing, and prioritised two as the most critical issues, namely approaches to and completeness of testing for End-to-End (E2E) ADS. We analyzed 17 research studies relevant to these two challenges, most of which focus on generating critical testing scenarios, and subsequently assessed their relevance and applicability in practice. Our study provides the first practitioner-driven review and evaluation of current ADS testing research, reveals practical challenges in ADS testing, offers rapid insights for practitioners, and highlights the need for more context-aware, industry-relevant solutions to bridge the gap between research and practice. ## Beyond End-to-End Recovery: Towards a Better Understanding of LLM Steganography via Rolewise Decomposition DevFeed: [Beyond End-to-End Recovery: Towards a Better Understanding of LLM Steganography via Rolewise Decomposition]() Original publisher: [Read original article]() Author: Om Tailor, Yucheng Pan, Solaleh Mohammadi, Xinyan Liu, Kaiqing Zhang Published: 2026-10-23T04:00:00Z Content type: article Language: en Sources: [arXiv: Cryptography and Security]() Topics: [LLM security](), [Large Language Model]() Tags: [cs-cl](), [cs-cr](), [cs-ma](), [decoder](), [decoding](), [e2e](), [encoding](), [evaluation](), [framework](), [instructions](), [language-model](), [large-language-model](), [llm](), [messages](), [patterns](), [private](), [recovery](), [responses](), [safety](), [steganography](), [text](), [token](), [transmission]() ### AI overview End-to-end recovery alone can hide whether LLM steganography fails at encoding or decoding, and cannot show how a private message is represented in transmitted text. The paper proposes evaluating these roles separately, including deterministic verification. Among 7,200 encoder responses, it reports 168 verifiable encodings that decoders missed and 706 responses from which private messages were reconstructed using word-length or token-initial patterns not specified in encoder instructions. The authors argue this separation can clarify steganographic behavior and safety concerns. ### Source excerpt arXiv:2610.30516v1 Announce Type: new Abstract: In large language model (LLM) steganography, an encoder hides a private message in an otherwise ordinary response, as text transmitted to a decoder, who attempts to recover it. The success of this private-message transmission is commonly measured through end-to-end (E2E) recovery, which checks whether the decoder correctly reconstructs the private message. However, E2E recovery cannot distinguish encoding failures from decoding failures or reveal how the private message is represented in the encoder's transmitted text. To address these limitations, we introduce a rolewise decomposition framework that separately examines how private messages are represented in encoder transmitted texts, what decoders recover, and what deterministic verification can establish. Across 7,200 encoder responses, we identify 168 verifiable encodings missed by decoders and 706 responses containing private messages reconstructed through word-length or token-initial patterns not explicitly specified in the encoder's instructions. These findings demonstrate that E2E recovery alone may obscure important distinctions between encoding and decoding, motivating their separate evaluation to better understand and mitigate the safety issues caused by LLM steganography. ## Building End-to-End Cypress Tests Across Browser, API, and Worker Boundaries DevFeed: [Building End-to-End Cypress Tests Across Browser, API, and Worker Boundaries]() Original publisher: [Read original article]() Author: Raju Dandigam Published: 2026-10-22T18:20:13Z Content type: tutorial Language: en Sources: [HackerNoon]() Topics: [Cypress](), [Test automation](), [API](), [Back end](), [Browser Automation](), [health check](), [hosting]() Tags: [api](), [api-testing](), [asynchronous](), [audit](), [backend](), [behavior](), [boundary](), [bounded](), [browser](), [browser-security](), [cypress](), [cypress-cy-request](), [cypress-cy-session](), [docker](), [e2e](), [effects](), [end-to-end-testing](), [integration-testing](), [react-application-testing](), [state](), [test-automation](), [tests](), [ui](), [worker](), [workflow]() ### AI overview For consequential workflows, verify three outcomes: what the user saw, what the system committed, and which asynchronous side effect occurred. The refund example uses HTTP requests to create fixtures and establish a session, the browser to approve the refund, and API checks to confirm durable state and an audit event. Poll worker-produced events with a bounded retry window based on service expectations, and use correlation IDs to avoid matching another parallel test. For GraphQL, check HTTP status, response errors, and business data separately. `cy.request()` synchronizes cookies but bypasses browser CORS and cannot be observed with `cy.intercept()`, so it cannot prove frontend request behavior. A Docker Compose test stack can preserve separate app and worker boundaries and use health checks for readiness; pin the Cypress image. Keep test endpoints isolated or narrowly authorized. This pattern verifies only the selected workflow and side-effect sink; it does not replace contract, load, security, or monitoring tests. ### Source excerpt Learn how to use Cypress to verify UI behavior, backend state, and asynchronous side effects so that passing tests reflect real workflow outcomes. ## StarMind: Challenges and Opportunities in Orbital Computing DevFeed: [StarMind: Challenges and Opportunities in Orbital Computing]() Original publisher: [Read original article]() Author: Zisheng Gong, Zhaoyu Liu, Houtianfu Wang, O. Tansel Baydas, Liu Cao, Xia Xu, Ozgur B. Akan Published: 2026-10-21T04:00:00Z Content type: article Language: en Sources: [arXiv: Networking]() Topics: [Distributed Systems & Parallel Computing](), [Artificial Intelligence](), [resource orchestration](), [High-Performance Computing](), [Big data infrastructure]() Tags: [ai](), [analysis](), [artificial-intelligence](), [capacity](), [case-study](), [challenges](), [computing](), [cs-ni](), [cs-sy](), [distributed](), [e2e](), [eess-sy](), [federated](), [framework](), [gateway](), [intelligence](), [interfaces](), [large](), [latency](), [layers](), [links](), [mission](), [model]() ### AI overview StarMind proposes a five-layer framework and six standardizable interfaces for coordinating distributed computing across heterogeneous orbital systems. In a representative SAR/InSAR case study, resource-aware task placement and gateway selection reduce end-to-end latency to 41 minutes, compared with 61 minutes for localized onboard processing and 146 minutes for ground processing. The improvement comes from reducing contact waits and urgent Earth downlinks, not from faster orbital processors. The approach is more effective with large source datasets, compact outputs, and sufficient optical inter-satellite link capacity; otherwise, conventional processing may be preferable. The article also identifies joint task partitioning and cross-domain orchestration, hierarchical federated intelligence, and large model-enabled intelligence as research opportunities. This overview reflects the supplied abstract. ### Source excerpt arXiv:2610.26916v1 Announce Type: new Abstract: Space-based artificial intelligence (AI) is moving beyond isolated onboard processing toward constellation-scale computing over optical inter-satellite links (OISLs). Yet current infrastructures remain fragmented, making end-to-end (E2E) coordination across heterogeneous and multi-vendor systems difficult. This article examines the StarMind vision and develops a reference framework for distributed orbital computing. It organizes the system into five layers and defines six standardizable interfaces that link mission intent to resource conditions and trustworthy result delivery. The framework also clarifies the operational constraints that determine whether orbital cooperation is worthwhile. In a representative synthetic aperture radar/interferometric SAR (SAR/InSAR) case study, the framework reduces E2E latency to 41-min, compared with 61-min for localized onboard computing and 146-min for ground-based processing. This gain does not rely on faster processors in orbit. Instead, it comes from reducing contact waiting and urgent Earth downlink traffic through resource-aware task placement and gateway selection. Feasibility analysis shows that the proposed framework is more effective when source datasets are large, output products are compact, and optical inter-satellite link capacity is sufficient. When these conditions are absent, conventional processing may remain preferable. Finally, we discuss research opportunities in joint task partitioning and cross-domain orchestration, hierarchical federated intelligence, and large model-enabled intelligence to advance an interoperable space-ground computing continuum. ## The Agents on Rails Files: designing black-box evals for coding agents DevFeed: [The Agents on Rails Files: designing black-box evals for coding agents]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-10-21T00:00:00Z Content type: article Language: en Sources: [Evil Martians]() Topics: [Test automation](), [mutation-testing](), [agentic-coding](), [Ruby on Rails](), [AI in frontend development](), [AI-assisted coding](), [Application Development](), [gpt-6-astra]() Tags: [agentic-development](), [agents](), [ai](), [apps](), [browser](), [code](), [coding](), [coding-agents](), [designing](), [dx](), [e2e](), [evals](), [llm-benchmarking](), [llms](), [mutation](), [mutation-testing](), [rails](), [robust](), [test](), [testing](), [tricks](), [ui](), [ui-ux](), [ux](), [verification]() ### AI overview For coding agent evals, write black-box checks against product requirements and calibrate them so varied valid interfaces pass while broken behavior fails. The article describes Campfire Bots, where a hybrid white-box suite sped development before browser E2E checks; equivalent UI mutants tested tolerance for harmless markup changes, while faulty mutants checked detection of broken requirements. For greenfield Quizzy, the authors combined accessibility requirements with a few HTML/CSS mockups to give deterministic tests stable anchors without fully specifying the interface. Browser-test techniques include waiting for form requests to complete before navigation, replaying authenticated write traffic as an anonymous user to check authorization, and manually closing WebSockets because CDP offline mode leaves persistent connections open. The authors recommend production-like runs, diverse model trials, mutation calibration, and reviewing helper timeouts. In their reported trials, Rust cost more than Rails on Campfire Bots, largely because its Rails-shaped port had about 13 times more code; frontier models solved both Quizzy stacks, while cheaper models solved Rails but not Rust. These ### Source excerpt Tips and tricks on writing robust evals for coding agents that test apps whose code we've never seen: different UI/UX contract strategies, UI mutation testing, browser-based E2E verification gotchas. ## Why Agent-Written Code Needs Product Verification DevFeed: [Why Agent-Written Code Needs Product Verification]() Original publisher: [Read original article]() Author: stack72 Published: 2026-10-06T14:45:14Z Content type: opinion Language: en Sources: [Swamp Club Feed]() Topics: [Test automation](), [Test-driven development](), [Spec Driven Development]() Tags: [acceptance](), [agent](), [agents](), [ai](), [api](), [building-with-ai](), [capabilities](), [code](), [code-verification](), [coding-agent](), [cost](), [cost-optimisation](), [coverage](), [cryptographic](), [defects](), [define](), [detection](), [domain](), [e2e](), [implementation](), [product](), [verification]() ### AI overview The article argues that reliable verification depends on independent expectations, not test level: an agent that writes both a feature and its tests can reproduce the same misunderstanding in either. It says agents have lowered the maintenance cost of end-to-end and integration tests and shortened repair cycles, making broader product verification more practical, though execution costs and user harm remain. Define user-facing capabilities from product definitions, research, or domain expertise outside the implementation loop, and protect expected outcomes from agent edits; agents can maintain test mechanics, with assertion or coverage changes reviewed independently. UAT, contract tests, or API checks can verify product needs, while unit tests remain useful for precise external rules such as pricing or permissions. Green unit suites alone do not establish that users can accomplish the needed workflows. ### Source excerpt Whether your tests catch real defects is not a property of the test level. It is a property of where the expectations come from and who can change them. The expectations have to come from outside the implementation loop -- and those expectations need to be protected. ## A Practical Guide to Automated Testing in Backend Systems DevFeed: [A Practical Guide to Automated Testing in Backend Systems]() Original publisher: [Read original article]() Author: Mehrad Published: 2026-10-02T14:45:46Z Content type: tutorial Language: en Sources: [HackerNoon]() Topics: [Software Engineering](), [Integration testing](), [Fuzzing/Fuzz testing](), [mutation-testing](), [Test automation](), [Unit testing]() Tags: [automated-testing](), [backend](), [backend-development](), [e2e](), [end-to-end-testing](), [feature-testing](), [fuzz-testing](), [guide](), [integration](), [integration-testing](), [property-based-testing](), [testing](), [unit-testing]() ### AI overview This guide explains how to choose backend tests based on the risks they cover and their cost. It describes unit, integration, feature, contract, end-to-end, characterization, property-based, fuzz, mutation, performance, smoke, and regression testing, with examples and trade-offs. It also discusses test maintenance, coverage, feedback speed, and balancing a test suite. ### Source excerpt A professional guide to Backend Automated Testing, covering Unit, Integration, Feature, Contract, E2E, Characterization, and Performance Testing. ## Introducing The Nx Cloud Timeline View DevFeed: [Introducing The Nx Cloud Timeline View]() Original publisher: [Read original article]() Author: Juri Strumpflohner Published: 2026-09-22T00:00:00Z Content type: release Language: en Sources: [Juri Strumpflohner]() Topics: [nx-cloud](), [CI/CD](), [observability](), [Apache Pulsar]() Tags: [ci](), [cpu](), [dependencies](), [distributed](), [e2e](), [efficiency](), [github](), [graph](), [memory](), [monorepo](), [nx](), [nx-cloud](), [pipeline](), [playwright](), [projects](), [visibility]() ### AI overview Nx Cloud's timeline view visualizes distributed CI runs, including task timing, idle periods, failures, retries, dependencies, and per-agent resource charts. The article shows how it can help teams find scheduling bottlenecks and rebalance work across a pipeline. ### Source excerpt Get full visibility into your distributed CI run with Nx Agents. ## EP226: API Concepts Every Software Engineer Should Know DevFeed: [EP226: API Concepts Every Software Engineer Should Know]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-19T15:31:06Z Content type: article Language: en Sources: [ByteByteGo]() Topics: [API](), [HTTP](), [GraphQL](), [gRPC](), [WebSocket](), [API keys](), [Cache](), [OAuth](), [observability](), [reliability-engineering](), [Documentation](), [Software Testing]() Tags: [api](), [caching](), [e2e](), [graphql](), [grpc](), [http](), [json](), [oauth](), [observability](), [software](), [software-engineer]() ### AI overview A system design newsletter explains API design fundamentals, including HTTP methods and status codes, request and response structure, REST, GraphQL, gRPC, webhooks, WebSockets, pagination, versioning, compatibility, authentication, reliability, caching, documentation, observability, and contract testing. ### Source excerpt Sending a request and reading JSON is one thing. Designing an API that other people can rely on is something where things get complicated. ## Using acceptance tests to catch regression bugs in legacy code before production DevFeed: [Using acceptance tests to catch regression bugs in legacy code before production]() Original publisher: [Read original article]() Author: Valentina Jemuović Published: 2026-09-16T07:33:58Z Content type: tutorial Language: en Sources: [Optivem Journal]() Topics: [Software Testing](), [Requirements](), [legacy systems]() Tags: [automated](), [bugs](), [e2e](), [legacy-code](), [regression](), [requirements](), [testing](), [ui](), [unit-tests]() ### AI overview This hands-on article explains why automated unit and end-to-end tests may still allow production bugs in legacy code. It presents acceptance test-driven development (ATDD), where requirements and tests describe the same observable behavior, making coverage clearer and reducing dependence on UI steps. ### Source excerpt You automated, and manual regression testing is still there. Those two facts have the same cause. ## All Dashboards Green, All Screens Black DevFeed: [All Dashboards Green, All Screens Black]() Original publisher: [Read original article]() Author: Whatnot Engineering Published: 2026-09-15T16:31:01Z Content type: article Language: en Sources: [Whatnot Engineering]() Topics: [observability](), [digital experience monitoring](), [Production Engineering](), [Grafana Cloud Frontend Observability]() Tags: [2026](), [black](), [cdn](), [client](), [client-side-security](), [component](), [dashboards](), [dependencies](), [distributed](), [distributed-systems](), [e2e](), [experience](), [external](), [green](), [incident](), [journey](), [livestream](), [observability](), [party](), [product](), [production](), [site-reliability-engineer](), [software-engineering]() ### AI overview A live update to a third-party security script caused black video screens for about 3,000 users in the first 30 minutes, while most component dashboards stayed green. End-to-end SLO monitoring detected the broken livestream journey and paged teams within five minutes. The system models each user action as a story: correlated events must show video, auction details, and chat within 60 seconds. A worker checks completed stories each minute and emits success metrics to Datadog. To prevent retries from skewing results, each client contributes its worst result per minute; expected outcomes such as a seller blocking entry are excluded from failures. Client telemetry uses local storage, compression, and retries, but fully offline clients cannot appear in live graphs. Instrumented stories reach about 99.5% SLI, and hundreds of thousands of events per minute are pushing ClickHouse queries toward their limit. ### Source excerpt Karol Gil | Reliability Platform (Poland) On June 8, 2026, newly loaded web clients began showing black screens instead of livestream video. For our platform, that's a serious problem: it's pretty hard to sell Pokémon cards that no one can see. It turned out that a third-party script we use for client-side security monitoring wasn't bundled with our release, but was rather fetched live from the provider's CDN. When the provider updated the script all new web clients fetched it, and it included an additional URL validation which our video provider didn't pass. The result? Black screens for users of the affected web clients, with most internal dashboards staying green. 3,000 users were impacted in the first 30 minutes of the incident. One system did catch it. Our end-to-end service-level objective (E2E SLO) monitoring was already in production and paged the owning teams within five minutes. Here's what it saw. The real problem Most of our dashboards stayed green because they monitor component-level health: a server, an endpoint, a specific function. These are all useful, but can all be healthy while the actual user experience is completely broken. This problem gets worse the more external dependencies there are, or the more sophisticated an experience you want to deliver. In complex, integrated product experiences like ours, a "small" problem can have an outsize impact on the user experience. Measuring this requires a different approach to observability, namely, to model the user journey across multiple surfaces that must be true for a customer to have a good experience. So how do we measure this in a complex distributed application? Joining a livestream is not one thing Joining a livestream sounds like one action, but the user expects at least three things: Video to be playing Auction details to be shown Chat to be visible and up to da Each of those can succeed or fail completely independently of the other two. Our video depends on third-party providers and CDN netwo ## Cypress Ambassador Spotlight: Maksym Donets DevFeed: [Cypress Ambassador Spotlight: Maksym Donets]() Original publisher: [Read original article]() Author: Jessica Capuano Mora Published: 2026-09-10T16:12:22Z Content type: article Language: en Sources: [Cypress Blog]() Topics: [Cypress](), [Software Testing](), [Test automation](), [developer-relations]() Tags: [advice](), [ambassador](), [automation](), [bugs](), [cloud](), [community](), [community-ambassadors](), [cypress](), [debugging](), [e2e](), [engineer](), [faster](), [feature](), [google](), [handling](), [help](), [helps](), [hobbies](), [program](), [spotlight](), [testing]() ### AI overview Maksym Donets says cy.session() is his favorite Cypress feature because it simplifies login and user switching while making tests easier to write and faster to run. He pairs it with cy.request() to speed up tests, and helps developers debug failed pipelines using Cypress Cloud. His advice to newcomers is to look up how to install Cypress. The excerpt also notes that he takes pride in catching almost all bugs with end-to-end testing. ### Source excerpt Welcome back to another edition of our Ambassador Spotlight series, where we shine a light on the Cypress community members who make testing better for everyone through the blogs they write, the talks they give, and the day-to-day help they offer to fellow developers. Curious about the program? You can learn more about it here. This time, we're catching up with Maksym Donets, an Automation QA Engineer with a straightforward philosophy on testing and an even more straightforward philosophy on li ## Don't Let AI Multiply Bad Tests DevFeed: [Don't Let AI Multiply Bad Tests]() Original publisher: [Read original article]() Author: Valentina Jemuović Published: 2026-09-01T08:22:21Z Content type: opinion Language: en Sources: [Optivem Journal]() Topics: [Software Testing](), [Test automation](), [Browser Automation]() Tags: [ai](), [bugs](), [domain](), [e2e](), [release](), [tests]() ### AI overview The article argues that AI can multiply brittle tests when it learns from a suite dominated by UI-coupled end-to-end tests. Such tests break when the interface changes and can add maintenance work without making releases safer or faster. The proposed sequence is to improve the existing tests by coupling them to the application domain rather than the UI, then use AI to generate more tests from that stronger foundation. The excerpt does not show the specific test examples referenced. ### Source excerpt AI learns from the tests you already have. If those are bad, you now get bad tests much faster. ## The Importance of Planning Before Coding: A Workflow Solution DevFeed: [The Importance of Planning Before Coding: A Workflow Solution]() Original publisher: [Read original article]() Author: adron Published: 2026-08-24T18:38:20Z Content type: opinion Language: en Sources: [Swamp Club Feed]() Topics: [agentic-coding](), [human-in-the-loop UX](), [qualitygates](), [Application Development](), [Claude Code]() Tags: [agentic](), [agents](), [ai](), [blast-radius](), [branch](), [changes](), [code](), [coding](), [coding-agent](), [docs](), [e2e](), [gate](), [gen-ai](), [git](), [git-worktree](), [idor](), [implementation](), [llms](), [multiple-agents](), [planning](), [programming](), [unknown-code-ramblings](), [workflow]() ### AI overview The article recommends gating coding agents behind an ask-plan-confirm workflow so they clarify ambiguity and get approval before editing. Agents first surface open questions, then propose a concise plan naming files and routes in scope, the approach, risks, tests, and any additive migration; implementation starts only after an explicit yes. Approval covers only that plan, so scope changes require re-planning and renewed approval. Questions may be skipped for genuinely unambiguous, low-risk requests. Agent-specific prompts adapt the gate to migrations, tests, e2e, docs, and blog work; read-only security and UX reviews clarify scope, then use the full gate if asked to make fixes. The author pairs this with isolated git worktrees. The excerpt argues the small upfront pause helps prevent unwanted diffs and scope creep. ### Source excerpt Learn how to prevent coding mishaps by implementing a structured planning process before AI agents make changes to your codebase. ## Everything You Need to Know About Patrol in 2026 DevFeed: [Everything You Need to Know About Patrol in 2026]() Original publisher: [Read original article]() Author: LeanCode Published: 2026-07-24T02:52:52Z Content type: article Language: en Sources: [Flutter Community]() Topics: [Flutter](), [Test automation](), [Integration testing](), [Continuous integration](), [mobile devops](), [CI/CD](), [UiAutomator](), [AI Integration Strategies](), [Visual Studio Code](), [Learning]() Tags: [ai](), [ai-powered](), [android](), [app](), [automation](), [automation-testing](), [ci](), [cloud](), [e2e](), [e2e-testing](), [flutter](), [flutter-app-development](), [framework](), [infrastructure](), [ios](), [junit](), [multiplatform](), [native](), [notifications](), [open-source](), [package](), [permission](), [ui-testing]() ### AI overview Patrol combines Flutter widget testing with native UI automation so a single end-to-end test can cover Flutter screens and elements outside the Flutter runtime, such as permission prompts, notifications, and other native UI. Its Dart tests run with Flutter testing APIs alongside native runners--XCUITest on iOS and UIAutomator on Android--which communicate over localhost. The excerpt describes Patrol 4.0 support for Android, iOS, and Web, plus a VS Code extension for running and debugging tests and Patrol MCP tools for AI-assisted test execution and inspection. Native test infrastructure enables compatibility with device farms and JUnit reporting; test bundling, sharding, parallel execution, artifact collection, and duration reporting support larger runs. The excerpt also presents Patrol Finders as a more concise alternative to Flutter's built-in finders, with waits for dynamic UI. It does not provide a full feature-by-feature account of Web support or the listed integrations. ### Source excerpt Author: Katarzyna Szulc from LeanCode Patrol has come a long way from being "just another Flutter testing tool." Today, it powers end-to-end testing across Android, iOS, and Web, integrates with native testing infrastructure, runs on cloud device farms, offers AI-powered testing capabilities, and continues to push the boundaries of what's possible in the Flutter ecosystem. No matter if you've been following Patrol for a while or you're simply looking for the best way to handle Flutter E2E testing, this article is the perfect place to catch up. What exactly is Patrol? Let's break it down Developed by LeanCode, Patrol is a powerful, open-source, multiplatform E2E UI testing framework for Flutter apps that overcomes the limitations of integration_test by handling native interactions. Let's dig deeper into that. Flutter's greatest strength also creates one of its biggest testing challenges. Unlike native apps, Flutter renders its entire UI using its own graphics engine instead of platform-native components. This enables a consistent, pixel-perfect experience across devices, but it also means that native UI automation tools often see a Flutter app as little more than a single canvas. They simply cannot interact with Flutter widgets. The official solution is the integration_test package, which gives tests direct access to the Flutter widget tree. As long as everything happens inside Flutter, it works well. But real apps rarely stay there. System permission dialogs, biometric prompts, notifications, WebViews, and other native UI all exist outside the Flutter runtime, making them inaccessible to integration_test. Patrol bridges this gap. It lets tests interact with both Flutter widgets and native UI elements in a single test. Because Patrol builds on native frameworks such as JUnit and XCTest, it also integrates seamlessly with existing CI pipelines, device farms, and the broader mobile testing ecosystem. If you are new to automating E2E testing in Flutter, you can start by ## The Electron browser is being deprecated in Cypress DevFeed: [The Electron browser is being deprecated in Cypress]() Original publisher: [Read original article]() Author: The Cypress Team Published: 2026-07-10T15:15:58Z Content type: release Language: en Sources: [Cypress Blog]() Topics: [Test automation](), [Electron](), [Software Testing](), [browser](), [Chromium](), [Google Chrome](), [Firefox](), [Edge](), [CI/CD](), [deprecated]() Tags: [automation](), [browser](), [browsers](), [chrome](), [chromium](), [ci](), [circleci](), [configuration](), [continuous-integration](), [cypress](), [deprecated](), [docker](), [docker-images](), [e2e](), [electron](), [future](), [major](), [releases](), [results](), [test](), [version]() ### AI overview Cypress 16.0.0 deprecates bundled Electron as a test browser; removal is planned for a future major version. Cypress cites Electron's lag behind current Chrome, behavior differences that can make tests pass in Electron but fail in Chrome, and delayed security updates. Teams should install a supported browser and select it with `defaultBrowser` or `--browser`, update CI and Docker setups where needed, and remove explicit Electron settings and workarounds. Until removal, Electron runs continue with a warning; afterward, runs targeting it or falling back to it will fail. Cypress recommends Chrome for Testing where possible for pinned, reproducible automation runs. The excerpt says teams already using installed browsers are unaffected. ### Source excerpt We're deprecating the Electron browser in Cypress, starting in Cypress 16.0.0, with removal in a future major version. After years of seeing inconsistent results between Electron and the browsers people actually use, we are standardizing on installed browsers for more reliable test results. ## devrig: MCP Steroid Goes Level 3 -- Autonomous IDE Setup for AI Agents DevFeed: [devrig: MCP Steroid Goes Level 3 -- Autonomous IDE Setup for AI Agents]() Original publisher: [Read original article]() Author: Eugene Petrenko Published: 2026-06-02T00:00:00Z Content type: article Language: en Sources: [Eugene Petrenko]() Topics: [devrig](), [Model Context Protocol](), [mcp-steroid](), [IntelliJ IDEA](), [agentic workflows](), [Claude](), [Keycloak]() Tags: [agent](), [agentic-experience](), [agents](), [ai](), [ai-agents](), [ai-coding](), [autonomous](), [claude](), [cli](), [command](), [config](), [devrig](), [e2e](), [eugene-petrenko](), [ide](), [install](), [intellij](), [jonnyzzz](), [keycloak](), [mcp](), [mcp-steroid](), [prompt](), [setup](), [tag-41199d53f463](), [test]() ### AI overview MCP Steroid 0.100's `devrig` CLI is presented as automating IDE setup for AI agents: `devrig install` writes the MCP config, while backend download/start can provision an IntelliJ-family IDE. The excerpt says the agent can discover running IDEs and route calls across their projects and windows, avoiding manual port selection; a required condition mentioned is that running IDEs have the MCP Steroid plugin installed. In the described end-to-end test, Claude provisions IntelliJ Community, opens Keycloak, and finds 4,633 PSI usages of a class in one prompt. The supplied excerpt does not include the promised trade-offs or further details. ### Source excerpt The new `devrig` CLI in MCP Steroid 0.100 turns the IDE bring-up ritual into one stdio command. `devrig install ` writes the MCP config; `devrig backend download/start` brings an IntelliJ-family IDE into existence and gives the agent an IDE of its own. In the e2e test, Claude provisions an IDE and finds 4,633 PSI usages of one class in Keycloak -- in one prompt. ## String Types For E2E Tests DevFeed: [String Types For E2E Tests]() Original publisher: [Read original article]() Author: Gleb Bahmutov Published: 2026-03-21T04:00:00Z Content type: tutorial Language: en Sources: [Gleb Bahmutov]() Topics: [TypeScript](), [Code](), [test](), [Exception](), [Visual Studio Code]() Tags: [code](), [cypress](), [e2e](), [exception](), [process](), [testing](), [types](), [typescript]() ### AI overview This tutorial explains how TypeScript string template literal types can distinguish formatted identifiers in end-to-end tests. It also shows their limitations for validating phone numbers and demonstrates more precise digit-based string types. ### Source excerpt Every individual item sold on Mercari.com has an id that looks like m. [Next page]()