# Early Stage Published articles for Early Stage. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Microsoft's Imagine Cup 'Evolves' From Student Competition To AI Startup Contest DevFeed: [Microsoft's Imagine Cup 'Evolves' From Student Competition To AI Startup Contest]() Original publisher: [Read original article]() Author: BeauHD Published: 2026-11-05T16:00:00Z Content type: news Language: en Sources: [Slashdot]() Topics: [Microsoft](), [Azure]() Tags: [account](), [ai](), [ai-startup](), [award](), [azure](), [categories](), [competition](), [contest](), [early-stage](), [founders](), [industry](), [microsoft](), [rules](), [startup](), [startups](), [student](), [students]() ### AI overview For 2027, Microsoft is shifting Imagine Cup from a student-focused competition to an AI startup contest open to eligible early-stage startups; founders need not be students. The competition has seven categories. Eligible student-led startups are encouraged to enter the Student Startup category, though they may choose an industry category instead. The overall top startup receives $250,000 and $1 million in startup credits. Student Startup entrants cannot win the open-category prizes: they compete for one award of $25,000 and $250,000 in credits. All entrants must have a valid Azure Subscription ID and a Microsoft for Startups account. ### Source excerpt theodp writes: That Was Then: The Imagine Cup competition, Microsoft explained in a 2003 press release, "seeks to provide students [aged 16-and-up] around the world with resources to use technology and Web services to solve everyday problems [...] Imagine Cup is about empowering students to apply their creativity and knowledge toward making the world a more-connected place." This Is Now: "Building is Hard. Selling is Harder." begins the "Why Imagine Cup?" promo video for the 2027 Imagine Cup competition, which is pivoting to an AI startup-first focus in recognition that "A new era for founders is here." "Since 2003, Imagine Cup has empowered emerging innovators to turn ambitious ideas into real-world solutions with Microsoft technology," Microsoft writes of the Imagine Cup Evolution. "Now part of Microsoft for Startups, Imagine Cup enters its next chapter in 2027, opening the competition to eligible early-stage startups and founders everywhere." An Eligibility FAQ drives home the point that Imagine Cup is no longer a student-only competition: "Imagine Cup 2027 welcomes eligible early-stage startups across seven categories; founders do not need to be students." And if you think making high school students compete against seasoned full-time employees of startup companies seems a tad unfair, Microsoft advises: "Eligible student-led startups are encouraged to enter the Student Startup category but may choose an industry category instead." The #1 overall startup will win a $250,000 cash award and $1,000,000 in startup credits. However, Student Startup category entrants are ineligible for the open category prizes (where the 2nd-place winner gets $50,000 cash and $350,000 in credits, while 3rd place is good for $25,000 and $350,000 in credits); they will instead compete for a single prize of $25,000 cash and $250,000 in credits. The Official Rules (PDF) note that all startups must have a valid Azure Subscription ID and a Microsoft for Startups account. Read more of this st ## rTaste: Do Research Agents Have Taste? Evaluating Early Stage Research Judgment DevFeed: [rTaste: Do Research Agents Have Taste? Evaluating Early Stage Research Judgment]() Original publisher: [Read original article]() Author: Art Kanke Published: 2026-11-04T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [AI Agent](), [Frontier AI](), [audit]() Tags: [agents](), [cs-ai](), [cs-cl](), [early-stage](), [evaluation](), [experiment](), [nlp](), [research]() ### AI overview The study tests whether research agents can identify promising experiments before seeing results. Six frontier models evaluated seven recent NLP papers by proposing and ranking minimum viable experiments (MVEs) and forecasting whether each would clear a self-set threshold; executors attempted all proposals, and three model auditors judged outcomes. Of 415 MVEs, 194 were judged sound, valuable, and new, but only 73 were also considered beyond the seed paper and non-obvious. First-ranked choices aligned with auditors' top choices more often than random selection, but showed no demonstrated advantage over choosing the first experiment written. Forecasts beat a base rate but were not measurably more accurate than forecasts from a fresh instance of the same model given only the paper and registrations. Teams cost more without clear overall gains in proposal quality, leaving evidence for ranking-based selection limited. ### Source excerpt arXiv:2610.40488v1 Announce Type: cross Abstract: Do research agents have taste, the judgment of which experiments are worth running before their outcomes are known? We give six frontier models seven recent NLP papers. Each model, working as a research agent alone or as a team, proposes minimum viable experiments (MVEs), forecasts whether each will pass a threshold it sets and ranks them. A separate executor then attempts every MVE, including those ranked last, and three model auditors judge the results. Of 415 MVEs, 194 are judged sound, valuable and new, but fewer than half of these (73) are also judged beyond the seed paper and non obvious. The agents' first choices match the auditors' most valued experiment more often than a random pick, but are not shown to improve on choosing the experiment written first. Their forecasts beat a base rate but are not measurably more accurate than those of a fresh instance of the same model given only the paper and registrations. Teams cost more without a clear overall improvement in proposal quality. The agents produce many worthwhile experiments, but the evidence that their rankings improve the choice of which to run remains limited. The evaluation harness and a dataset of all 415 MVEs, with their forecasts and audited outcomes, are available at https://github.com/ArtKanke/rTaste. ## What Drives the Zero-to-One Jump? Diagnosing and Steering Hard-Prompt Learning in RLVR DevFeed: [What Drives the Zero-to-One Jump? Diagnosing and Steering Hard-Prompt Learning in RLVR]() Original publisher: [Read original article]() Author: Rui Zhu, Yumou Liu, Zhenzhe Zheng, Fan Wu Published: 2026-11-03T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence](), [arXiv: Machine Learning]() Topics: [rlvr](), [grpo](), [llm-reasoning](), [Reinforcement learning]() Tags: [abstract](), [analysis](), [arxiv](), [based](), [can](), [code](), [cs-ai](), [cs-lg](), [discovery](), [driver](), [early-stage](), [entropy](), [event](), [first](), [fixed](), [generation](), [learning](), [one](), [prompt](), [rlvr](), [steering](), [zero]() ### AI overview The study finds that hard prompts approaching their first correct solution tend to produce failed rollouts with higher early-stage generation entropy than dead-end failures. Using this signal, NAPO integrates with GRPO to rank negative rollouts in unsolved groups and assign graded advantages, adding informative learning signals where standard group-relative updates vanish. Under fixed rollout budgets, it accelerates early solution discovery and raises Pass@256 by 10.0 points on AIME24 and 6.6 points on AIME2025. These findings suggest that zero-to-one transitions can be diagnosed and steered through driver-guided negative learning. ### Source excerpt arXiv:2611.02747v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has substantially improved mathematical reasoning, yet progress on hard prompts often stalls: a prompt can remain unsolved for many training steps before abruptly transitioning to solved--a discrete "zero-to-one" jump whose underlying dynamics and steerability remain poorly understood. We investigate two questions: what drives this transition, and can it be steered? Through event-based analysis measuring the time-to-first-positive (TTFP), we find that negative rollouts preceding successful transitions consistently retain higher early-stage generation entropy, distinguishing near-solvable failures from dead ends. This signal motivates NAPO (Negative-Augmented Policy Optimization), a method integrated with Group Relative Policy Optimization (GRPO) that ranks negatives in unsolved groups via an early-branching driver and assigns graded advantages, injecting informative gradients precisely where standard group-relative updates vanish. Under fixed rollout budgets, NAPO accelerates early solution discovery and improves Pass@256 by 10.0 points on AIME24 and 6.6 points on AIME2025, demonstrating that zero-to-one transitions are diagnosable and steerable through driver-guided negative learning. Our code is available at https://github.com/jameszhu-pixel-time/Z2O. ## ORACLE: Anticipating Scams from Partial Trajectories in Streaming App Usage DevFeed: [ORACLE: Anticipating Scams from Partial Trajectories in Streaming App Usage]() Original publisher: [Read original article]() Author: Wenbo Gao, Songbai Tan, Zhongan Wang, Fei Shen, Gang Xu, Huiping Zhuang, Yunyun Yang, Ming Li, Xiaofeng Zhu Published: 2026-11-02T05:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [online-fraud-prevention](), [streaming data analytics]() Tags: [agentic](), [benchmark](), [cs-cy](), [cs-lg](), [distillation](), [early-stage](), [experiments](), [fraud](), [scams](), [streaming]() ### AI overview ORACLE aims to warn about smartphone scams before intent is explicit by reasoning over partial, streaming app-usage histories. Its benchmark covers 12 scam types across 95 apps, with trajectories averaging 15 days and mixing normal and scam behavior. The framework uses a self-evolving context manager to consolidate entity-centered interactions over time, and on-policy self-distillation: a teacher with anti-scam reflections and skill-based clues supervises a student that lacks those reflections. The abstract reports improved early scam anticipation, with timely warnings and fewer false alerts in realistic streaming scenarios; it provides no quantitative results. ### Source excerpt arXiv:2605.16363v2 Announce Type: replace Abstract: Smartphone scams are increasingly prevalent and typically manifest as multi-stage, cross-application processes with gradually emerging intent. Effective intervention thus requires anticipating scams before the intent becomes explicit. This is inherently challenging, as decisions must rely on partial trajectories with temporally distributed evidence. In this paper, we propose \textbf{ORACLE} Online Reasoning for Anticipating Cross-temporal Latent thrEats, the first agentic framework for early scam anticipation from \textit{streaming app-usage} trajectories. To support this setting, we curate a real-world long-horizon benchmark of streaming app-usage trajectories, covering 12 scam types, spanning extended periods (15 days on average), involving diverse applications (95 apps), and interleaving normal and scam behaviors. To address fragmented evidence, we introduce a self-evolving context manager that adaptively consolidates entity-centric interactions over time, enabling more effective reconstruction of cross-temporal evidence from partial observations. To enhance sensitivity to latent early-stage signals, we propose an on-policy self-distillation scheme in which a teacher model, conditioned on summarized anti-scam reflections and clues by skills, supervises a student model without access to such reflections. This scheme thereby distills evidence-informed knowledge and improves recognition of emerging fraud patterns from partial trajectories. Experiments show that \method{} consistently improves early scam anticipation, yielding timely warnings while reducing false alerts in realistic streaming scenarios. ## LLM-Driven, Datasheet-Aware Automated Hardware Compatibility Verification for Early-Stage, Pre-Schematic Embedded System Design DevFeed: [LLM-Driven, Datasheet-Aware Automated Hardware Compatibility Verification for Early-Stage, Pre-Schematic Embedded System Design]() Original publisher: [Read original article]() Author: Haotian Qiao, Robert P. Dick Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Embedded Systems](), [Large Language Model](), [Formal verification](), [Specifications]() Tags: [abstract](), [analysis](), [arxiv](), [automated](), [automation](), [based](), [but](), [can](), [compatibility](), [component](), [connectivity](), [cs-ai](), [cs-sy](), [design](), [early-stage](), [eess-sy](), [embedded](), [hardware](), [llm](), [stage](), [system](), [system-design](), [verification]() ### AI overview The framework checks for documentation-level interface incompatibilities before schematic simulation or implementation, using datasheets and high-level component connections. It builds a design graph of device connections and shared interaction domains, retrieves properties needed for explicit verification criteria, then evaluates compatibility with deterministic scripts. Modular stages preserve intermediate results and avoid relying on an LLM for numerical computation. On seven embedded-system designs using 34 datasheets, it reports 97.5% verification accuracy and 8.6 times less input context than "upload-and-query" workflows. These findings are limited to the reported evaluation. ### Source excerpt arXiv:2608.25217v2 Announce Type: replace Abstract: We present an LLM-driven, datasheet-aware framework for early-stage hardware compatibility verification that identifies documentation-level interface incompatibilities based on hardware datasheets and high-level component connectivity descriptions. It does not require, and can therefore be used, before detailed schematic simulation and implementation. We view trustworthy LLM-assisted design automation not as directly generating answers from documents, but as transforming engineering information through traceable verification stages. Given hardware datasheets and high-level component connectivity descriptions, the framework constructs a design graph that captures device connectivity and shared interaction domains, retrieves only the engineering properties required by explicit, domain-oriented verification criteria , and generates deterministic scripts for compatibility evaluation. By decomposing compatibility analysis into modular stages and preserving intermediate results, the framework reduces context overhead, improves transparency and tractability, enables scaling, and avoids reliance on LLMs for numerical computation. Evaluated on seven embedded-system designs comprising 34 datasheets, our framework achieves 97.5% compatibility-verification accuracy and an 8.6 times reduction in input context size compared with ``upload-and-query'' workflows. These results demonstrate the feasibility of LLM-assisted, specification-based hardware compatibility verification at an early design stage, as well as the need for, and substantial benefits of, modular task decomposition, formalized verification criteria, and task-aware compact context construction. ## From Reconnaissance to Response: Quantitative Risk Parameterization and Game Theoretic Containment in Modern Enterprise Attack DevFeed: [From Reconnaissance to Response: Quantitative Risk Parameterization and Game Theoretic Containment in Modern Enterprise Attack]() Original publisher: [Read original article]() Author: Shadeeb Hossain, Elias Calboreanu Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Cryptography and Security]() Topics: [Security](), [threat detection](), [Security Information and Event Management (SIEM)](), [Cortex XSOAR](), [Operations research and optimization](), [streaming data analytics](), [Statistics]() Tags: [2023](), [adversarial](), [analysis](), [attack](), [automated](), [bayesian](), [control](), [cost](), [cs-cr](), [cs-gt](), [cs-sy](), [cyber](), [cyber-incident](), [data](), [defender](), [defense](), [early-stage](), [eess-sy](), [enterprise](), [exploitability](), [factor](), [game](), [reconnaissance](), [risk]() ### AI overview The abstract describes a decision engine that combines vulnerability-based attacker success estimates with live SIEM telemetry to decide when proactive containment is justified. It maps CVSS exploitability parameters to success probabilities, uses FAIR Monte Carlo simulations to evaluate defender log distributions, and models SIEM log arrivals as a Poisson process to update threat beliefs through sequential Bayesian filtering. A dynamic Bayesian Stackelberg game yields a threshold for containment when expected unmitigated risk exceeds its cost. Simulations parameterized with data from the 2023 MGM Resorts and Caesars incidents reportedly filter transient background noise and trigger SOAR network isolation within seconds of probing. The excerpt reports sensitivity to live perimeter vulnerability but does not provide implementation details or full evaluation results. ### Source excerpt arXiv:2609.36485v3 Announce Type: replace-cross Abstract: Modern Security Operations Centers struggle with delayed manual incident response, enabling adversaries to advance through the Cyber Kill Chain during early stage reconnaissance. While classical game theoretic defense models optimize strategic resource allocation, they rely on static utility matrices that fail to adapt to dynamic telemetry. This paper presents an integrated, metrics driven decision engine that bridges quantitative risk parameterization and continuous automated response time. Common Vulnerability Scoring Systems exploitability parameters are mapped to attacker success probabilities and evaluate defender log distributions via Factor Analysis of Information Risk Monte Carlo simulations. Real time SIEM logs streams are modeled as Poisson process arrival rates, dynamically updating defender posterior threat belief through sequential Bayesian filtering. A closed form threshold is derived by framing the interaction as a dynamic Bayesian Stackelberg game, where the expected unmitigated risk exceeds proactive containment cost. Parameterized against empirical data from the 2023 MGM Resorts and Caesars Entertainment cyber incident, simulation results demonstrate that the engine suppresses transient background noise while triggering automated SOAR network isolation within seconds of adversarial probing. Multi parameter sensitivity analysis confirms that the decision boundary dynamically adjusts to live perimeter vulnerability, offering a control theoretic foundation for sub minute automated threat containment. ## Wiz's CTO on PHP2Shell and the coming 'vulnpocalypse' DevFeed: [Wiz's CTO on PHP2Shell and the coming 'vulnpocalypse']() Original publisher: [Read original article]() Author: Theresa Loconsolo Published: 2026-10-28T19:05:46Z Content type: news Language: en Sources: [TechCrunch | AI News & Artificial Intelligence]() Topics: [code security](), [Cybersecurity](), [Open Source](), [open source maintenance](), [cyber resiliency]() Tags: [ai](), [appsec](), [bugs](), [calif](), [cleanup](), [critical](), [cto](), [cybersecurity](), [early-stage](), [equity-video](), [exposure-management](), [google](), [open-source](), [openai](), [organizations](), [patched](), [patching](), [php](), [podcast](), [scale](), [security](), [startups](), [vulnerabilities](), [wiz]() ### AI overview Two critical PHP bugs triggered an urgent response, with some organizations taking sites offline while they patched. The excerpt says researchers expect AI to speed up the discovery and exploitation of new vulnerabilities. In a TechCrunch Equity episode, Wiz CTO Tomer Schwartz describes the response involving Wiz, OpenAI, Calif, and the PHP team, and discusses security for early-stage startups and why manual patching will not scale. ### Source excerpt The PHP scare may have faded from your feed, but the cleanup is far from over. Last week, two critical bugs in the open source language behind roughly 70% of websites left organizations scrambling to fix their systems, with some taking sites offline until the vulnerabilities could be patched. According to researchers who found the [...] ## W3C launches second pilot of program to support Open Source projects DevFeed: [W3C launches second pilot of program to support Open Source projects]() Original publisher: [Read original article]() Author: Ian Jacobs, Strategy Team Published: 2026-10-27T11:50:00Z Content type: news Language: en Sources: [W3C]() Topics: [W3C](), [polyfill](), [Open Source](), [open source maintenance]() Tags: [agentic](), [agents](), [assistive-technologies](), [blogs](), [community](), [coverage](), [declarative](), [developers](), [early-stage](), [events](), [experiment](), [feedback](), [governance](), [interoperability](), [javascript](), [lifecycle](), [npm](), [open-source](), [pilot](), [polyfill](), [w3c](), [webmcp]() ### AI overview W3C's second Open Source Program pilot is a polyfill for the incubating WebMCP API, intended to improve interoperability and let developers experiment while the specification evolves. WebMCP lets developers expose application functionality as JavaScript tools with natural-language descriptions and structured schemas for agents, browser agents, and assistive technologies. Initial goals are to publish the polyfill as an npm package, advance lifecycle events and declarative support, track specification changes, and maintain Web Platform Tests coverage. If WebMCP moves into standardization and native implementations provide interoperable support, the polyfill would be phased out. W3C also launched a Community Group to guide the program's growth and governance; participation is open without W3C Membership. ### Source excerpt In this blog post, Ian Jacobs, Strategy Team, introduces the second pilot of the W3C Open Source program: a polyfill for the WebMCP API. WebMCP is a new JavaScript interface that allows web developers to expose their web application functionality as "tools". ## DealsFlow: An Evidence-Gated Agentic Harness for Venture Capital Deal Triage DevFeed: [DealsFlow: An Evidence-Gated Agentic Harness for Venture Capital Deal Triage]() Original publisher: [Read original article]() Author: Ming Ze Tang, David Cutting, Boyd Carson, Vasiliki Carson Published: 2026-10-26T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Agent Harness](), [Retrieval Augmented Generation (RAG)](), [Finance](), [llm-reasoning]() Tags: [agentic](), [analyst](), [application](), [arxiv](), [audit](), [code](), [cs-ai](), [deal](), [early-stage](), [harness](), [human-in-the-loop](), [llm](), [triage](), [venture-capital]() ### AI overview DealsFlow uses an LLM to assess pitch decks and cite evidence, while code combines category ratings and limits factual scores when external corroboration is missing. A sceptic review can lower scores, and evidence is retrieved before a single generation pass. On 142 investments at least three years old, its scores correlated with total-value multiples at Spearman ρ=0.45 and identified write-offs with AUC 0.71, modestly above an unconstrained LLM (ρ=0.38; AUC 0.68); most of the gain came from the sceptic call, and the study did not test whether its lower-only rule mattered. In 20 inbound applications, analyst time including verification and correction fell from 44.7 to 18.9 minutes per application. Of 240 cited claims, 84% were fully supported, while 70% of reports needed at least one correction. The authors caution that retrieval was not date-restricted, potentially overstating screening performance. ### Source excerpt arXiv:2610.32380v1 Announce Type: new Abstract: Large language models (LLMs) can read startup pitch decks, but letting them emit investment scores directly invites score inflation, run-to-run variance and rationales that are hard to audit. We describe DealsFlow, a human-in-the-loop agentic harness deployed at a UK early-stage fund. The LLM reads the deck, cites evidence and rates each category; code combines the ratings, caps them and only ever lowers them. Three mechanisms implement this: a stage-aware corroboration ceiling (78, 70 or 60 out of 100) on factual categories without external evidence; a sceptic call that may lower but never raise a score; and a pre-fetch pattern that retrieves and indexes all evidence before a single generation pass. We evaluate on $N=243$ SEIS/EIS investments with recorded outcomes. Because recent investments have unresolved outcomes, we use the 142 that are at least three years old. There, the full pipeline's score has Spearman $\rho=0.45$ (95% CI 0.32 to 0.57) with the total-value multiple and AUC 0.71 for identifying write-offs, against $\rho=0.38$ and AUC 0.68 for an unconstrained LLM. The gain is small and comes mostly from the sceptic call; we did not test whether its lower-only rule matters. Investments that later exceeded $2\times$ had 64% of factual categories corroborated, against 38% for write-offs (omnibus Kruskal-Wallis test over five outcome groups, $p=0.031$), and our retrieval was not date-restricted, so these figures may overstate screening skill. In a study of 20 inbound applications, analyst time per application fell from 44.7 to 18.9 minutes including verification and correction, and 84% of 240 cited claims were fully supported by their sources, but 70% of reports needed at least one correction. ## Can AI Co-Design Structures? An AI Agent for Early-Stage Structural Design DevFeed: [Can AI Co-Design Structures? An AI Agent for Early-Stage Structural Design]() Original publisher: [Read original article]() Author: Ricardo Maia Avelino, Mennatallah El-Assady, Tom Van Mele, Philippe Block Published: 2026-10-23T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [AI Agent](), [Artificial Intelligence](), [Large Language Model](), [data-modeling](), [3D]() Tags: [agent](), [agentic](), [agentic-workflows](), [ai](), [ai-agent](), [ai-assisted](), [api](), [artifacts](), [arxiv](), [building](), [co-design](), [cs-ai](), [data-structures](), [data-types](), [design](), [domain](), [early-stage](), [elements](), [generation](), [graph](), [language]() ### AI overview The paper presents a preliminary prompt-to-structure workflow in which an AI agent uses a COMPAS-based structural modelling API through an MCP server. Rather than generating only visual mesh geometry, the agent performs controlled operations on a graph-based model with explicit building elements. The resulting structures can be inspected, edited, serialized, and connected to downstream AEC workflows. Selected examples include progressive, editable sequences resembling iterative structural design. The authors frame this as a proof of concept for AI-assisted structural co-design, not a demonstrated mature design system. ### Source excerpt arXiv:2610.31113v1 Announce Type: new Abstract: Recent advances in large language models and agentic workflows offer a new paradigm to early-stage structural design translating design intent into structural models. Although text-to-3D approaches have demonstrated that natural language can be used to produce visual, mesh-based geometric models, structural design requires domain-specific geometric arrangements and semantically rich data types. This paper introduces a proof-of-concept workflow for prompt-to-structure generation using an AI agent connected to a COMPAS-based structural modelling API through a Model Context Protocol (MCP) server. Through controlled modelling operations, the agent creates and modifies a graph-based structural data model composed of explicit building elements. The resulting artifacts can be inspected, modified, serialised, and further connected to downstream AEC workflows. The approach is demonstrated through selected prompt-to-structure examples and by progressive and editable design sequences that resemble typical iterative structural design workflows. The results are preliminary, but demonstrate the possibility of combining natural language, agentic workflows, and domain-specific structural data structures as a first step toward AI-assisted structural co-design. ## NetGuise: Adversarial Traffic Generation for Early-Stage Classifier Evasion DevFeed: [NetGuise: Adversarial Traffic Generation for Early-Stage Classifier Evasion]() Original publisher: [Read original article]() Author: Binghui Wu, Dinil Mon Divakaran, Mohan Gurusamy Published: 2026-10-21T04:00:00Z Content type: article Language: en Sources: [arXiv: Cryptography and Security](), [arXiv: Networking]() Topics: [network-intelligence](), [Adversarial attacks](), [Internet Traffic]() Tags: [adaptive](), [adversarial](), [arxiv](), [bandwidth](), [censorship](), [classification](), [classifiers](), [cs-cr](), [cs-ni](), [early-stage](), [encrypted-traffic](), [environment](), [evaluation](), [evasion](), [execution](), [flow](), [framework](), [full](), [generation](), [history](), [interface](), [traffic]() ### AI overview NetGuise morphs encrypted traffic to evade early-stage classifiers using packet padding and non-negative delays while preserving direction, order, and packet count and respecting MTU, bandwidth, and latency constraints. It supports history-only causal execution and a full-window oracle upper bound. Targeted evasion varies sharply by interface: on VANT, 33.2% causal versus 82.0% oracle; on UTMobileNet, 5.6% versus 81.8%. NetGuise beats the strongest causal baseline, BAP, on both datasets; on VANT it lowers bandwidth overhead from 19.80% to 3.02% and generation latency from 37.42 to 1.16 ms per flow. Oracle results include up to 85.7% evasion, 43.5% after four adaptive censor retraining rounds, and 48% in zero-shot cross-environment transfer. These abstract-level results indicate that early-stage traffic can remain morphable, with effectiveness limited by observation conditions. ### Source excerpt arXiv:2610.27456v1 Announce Type: cross Abstract: Early-stage encrypted traffic classifiers make decisions from only the first few packets of a connection, enabling low-latency classification but raising robustness concerns under realistic traffic morphing. Existing adversarial traffic generation methods often assume complete flows, feature-level perturbations, or iterative runtime feedback, which do not match the constraints of early-stage execution. We present NetGuise, a target-conditioned traffic morphing framework that generates packet-level padding and non-negative delay actions while preserving packet direction, ordering, and packet count and respecting MTU, bandwidth, and latency constraints. We evaluate NetGuise for censorship evasion under two observation interfaces, i) a history-only causal interface for online packet-by-packet execution, ii) and a full-window oracle interface that provides an upper-bound characterization of early-stage morphability. On VANT, NetGuise achieves 33.2% mean targeted evasion under the causal interface versus 82.0% under the oracle interface; on UTMobileNet, the corresponding results are 5.6% and 81.8%. NetGuise outperforms the strongest causal baseline, BAP, on both datasets and, on VANT, reduces bandwidth overhead from 19.80% to 3.02% and generation latency from 37.42 ms/flow to 1.16 ms/flow. Under the oracle interface, it achieves up to 85.7% targeted evasion, retains 43.5% after four rounds of adaptive censor retraining, and maintains 48% under zero-shot cross-environment transfer. Our results show that early-stage classifiers can remain morphable despite high clean accuracy and that robustness evaluation should account for targeted morphability, observation constraints, defender adaptation, and operational network conditions. ## TechCrunch Disrupt 2026: Last day DevFeed: [TechCrunch Disrupt 2026: Last day]() Original publisher: [Read original article]() Author: TechCrunch Events Published: 2026-10-15T14:00:00Z Content type: news Language: en Sources: [TechCrunch | AI News & Artificial Intelligence]() Topics: [AI Integration Strategies](), [Business, Industry, Usability, User Experience, User Research, Web Strategy](), [Security Attacks]() Tags: [agent-security](), [ai](), [ai-inference](), [ai-infrastructure](), [apps](), [biotech-health](), [climate](), [early-stage](), [event](), [fintech](), [fundraising](), [media-entertainment](), [robotics](), [startups](), [techcrunch-disrupt](), [venture]() ### AI overview A promotional roundup of the final day of TechCrunch Disrupt 2026, listing sessions on AI product building, startup growth, agent security, AI adoption, and infrastructure alongside event logistics and ticket offers. ### Source excerpt Last day to be part of TechCrunch Disrupt 2026. Join 10,000+ tech leaders for a day of connections and insights. Save 75% on your pass now. ## Arm joins EuroCDP to accelerate Europe's next generation of innovators DevFeed: [Arm joins EuroCDP to accelerate Europe's next generation of innovators]() Original publisher: [Read original article]() Author: Vince Jesaitis Published: 2026-09-21T12:00:09Z Content type: article Language: en Sources: [Arm Newsroom]() Topics: [Arm](), [Product Management](), [Raspberry Pi RP2350]() Tags: [academia](), [chip-design](), [custom-silicon](), [early-stage](), [european-commission]() ### AI overview Arm is joining the European Chips Design Platform (EuroCDP), giving European startups, small and medium-sized businesses, and research teams access to Arm technology, software, tools, and ecosystem support for custom silicon development. The initiative also offers design engineering support, training, and routes to Arm IP through programs for eligible startups and academic research. ### Source excerpt Expanding access to Arm technology, tools and ecosystem support for European startups, SMEs and research teams developing custom silicon. The post Arm joins EuroCDP to accelerate Europe's next generation of innovators appeared first on Arm Newsroom. ## The 9 best tools for your early-stage startup tech stack in 2026 DevFeed: [The 9 best tools for your early-stage startup tech stack in 2026]() Original publisher: [Read original article]() Author: Alan McGlinchey Published: 2026-08-04T00:00:00Z Content type: article Language: en Sources: [Intercom]() Topics: [issue tracking](), [Slack]() Tags: [ai-tools](), [aws](), [customer-engagement](), [early-stage](), [growth](), [hubspot](), [partner](), [startup](), [startup-advice](), [startups](), [stripe](), [tech-stack](), [technology-stack]() ### AI overview A guide to selecting tools for an early-stage startup tech stack, with advice to favor tools that scale, integrate well, and solve a clear problem. It reviews options for payments, team communication, AI meeting notes, customer support, issue tracking, observability, databases, HR and IT, and sales. The article also promotes an early-stage program offering partner credits and discounts. ### Source excerpt Learn how to create a foundational tech stack for your high growth and early-stage startup - starting with these top tools. ## Introducing AppSignal for Startups DevFeed: [Introducing AppSignal for Startups]() Original publisher: [Read original article]() Author: Serena Chou Published: 2026-07-06T00:00:00Z Content type: release Language: en Sources: [AppSignal]() Topics: [Monitoring](), [observability](), [error tracking](), [log management](), [dashboards](), [Model Context Protocol](), [Command-line interface]() Tags: [dashboards](), [early-stage](), [error-tracking](), [log-management](), [mcp](), [mcp-server](), [monitoring](), [observability](), [platform](), [pricing](), [startups](), [teams]() ### AI overview AppSignal announces an ongoing discount for early-stage and bootstrapped startups, with larger discounts for Y Combinator-backed teams. The offer applies to the full monitoring platform, including application performance monitoring, error tracking, log management, uptime and process monitoring, host and frontend monitoring, anomaly detection, dashboards, and AppSignal Intelligence features. ### Source excerpt AppSignal for Startups gives early-stage teams an ongoing discount on the full platform, with a bigger deal for Y Combinator companies. ## Classiq and AWS Power Quantum-Classical Chemistry Innovation in Singapore with Hatch DevFeed: [Classiq and AWS Power Quantum-Classical Chemistry Innovation in Singapore with Hatch]() Original publisher: [Read original article]() Author: Roie Dann Published: 2026-06-22T13:40:35Z Content type: article Language: en Sources: [AWS Quantum Technologies Blog]() Topics: [Quantum Computing](), [Amazon Web Services](), [Amazon EC2](), [cloud-infrastructure](), [Computing]() Tags: [amazon-braket](), [amazon-ec2](), [amazon-elastic-block-store-amazon-ebs](), [aws](), [aws-batch](), [chemistry](), [classiq](), [cloud-infrastructure](), [compute](), [computing](), [customer-solutions](), [development](), [early-stage](), [energy](), [partner-solutions](), [quantum](), [quantum-algorithms](), [quantum-chemistry](), [quantum-classical](), [quantum-computing](), [quantum-research](), [quantum-technologies](), [research](), [singapore](), [storage](), [technical-how-to]() ### AI overview This post describes Classiq's quantum-classical pipeline for estimating molecular binding energies in computational chemistry. The workflow combines parallelized Density Functional Theory calculations with a variational quantum eigensolver, using Amazon EC2 cloud infrastructure for classical computation and quantum computing to account for quantum correlations. ### Source excerpt Introduction In biochemical processes development and analysis, binding energy, the energy released when a small molecule docks into a protein's active site, determines how strongly a compound, such as a ligand, interacts with its protein target. Early-stage computational prediction of this quantity helps research teams prioritize candidates before committing to resource-intensive laboratory testing. Conventional methods [...] ## Helping Startups Build Faster with an AI Startup Ecosystem DevFeed: [Helping Startups Build Faster with an AI Startup Ecosystem]() Original publisher: [Read original article]() Author: Courtney Gras Published: 2025-10-29T13:59:30Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [hosting](), [developer-relations](), [Developer Tools](), [AI Chat]() Tags: [accelerate](), [accessible](), [agentic](), [ai](), [ai-startup](), [build](), [build-faster](), [building-with-ai](), [cloud](), [community](), [costs](), [developer](), [digitalocean](), [droplets](), [early-stage](), [ecosystem](), [entrepreneurship](), [faster](), [founders](), [startups]() ### AI overview DigitalOcean's AI Startup Ecosystem combines discounted cloud infrastructure and tooling with technical support and go-to-market resources to help AI startups build, launch, and scale. It brings together technology partners, community organizations, AI developer platforms, and university entrepreneurship programs, with Techstars named as a founding partner. The excerpt says Techstars will support founders across their startup journey, including early-stage ideas through hackathons, but ends before describing further support. Uxify is cited as an example of a startup using DigitalOcean Gradient AI GPU Droplets and other products; it values the products' simplicity for reducing bureaucracy and helping it reach market sooner. ### Source excerpt At DigitalOcean, we've always been committed to supporting cutting-edge startups by providing them with accessible cloud solutions tailored to the needs of fast growing businesses. Some of the AI startups already growing alongside DigitalOcean include Uxify, who is building an AI-enabled website performance solution using DigitalOcean Gradient™ AI GPU Droplets and other products. They value how DigitalOcean's straightforward products enable them to focus on building their business, saying "As a startup, time to market is essential and the simplicity of DigitalOcean cut all the noise and removed a lot of bureaucracy compared to some of the other providers." As AI has become an integral part of today's leading startups, we're pleased to announce the evolution of our offerings for startups building with AI. Recognizing startups need more than just tools to build and launch amazing products, DigitalOcean's new AI Startup Ecosystem brings together established technology partners, vibrant community organizations, AI developer platforms, and university entrepreneurship programs to help founders build, launch and scale faster on DigitalOcean's comprehensive agentic cloud. The DigitalOcean AI Startup Ecosystem provides members with discounted infrastructure and tooling to help them build, while offering access to technical support and go-to-market resources to help them grow. Working with founding partners like Techstars, the AI Startup Ecosystem serves as a trusted partner to founders, removing barriers, reducing costs, and providing a scalable cloud platform that grows alongside its members. Startups, apply today through this link. How ecosystem partners can help accelerate AI startup growth As we continue to grow our AI startup offerings, we're excited to be deepening our existing partnerships for greater impact. For example, working with Techstars we'll support founders throughout their entire startup journey, from supporting early-stage ideas with hackathons, to offerin ## FAANG and Startup Engineering: How Scale, Delivery Processes, and Compensation Differ DevFeed: [FAANG and Startup Engineering: How Scale, Delivery Processes, and Compensation Differ]() Original publisher: [Read original article]() Published: 2025-05-19T00:00:00Z Content type: opinion Language: en Sources: [Gaurav Sarma's]() Topics: [distributed-systems](), [CI/CD](), [observability](), [Security Information and Event Management (SIEM)](), [Resilience](), [systems]() Tags: [career](), [ci-cd](), [distributed-systems](), [early-stage](), [experience](), [faang](), [metrics](), [monitor](), [process](), [resiliency](), [startups]() ### AI overview The author argues that experience at large technology companies can expose engineers to scalable distributed systems, standardized release practices, CI/CD, monitoring, and metrics. The article contrasts these processes with the faster delivery of early-stage startups, which may introduce more process as bugs and resiliency issues grow, and discusses compensation and equity differences. ### Source excerpt I have worked in early stage, late stage startups and bigger companies as well. "FAANG in your 20s often makes your skills deteriorate"... ## Prisma Launches Startup Program With Credits and Support for Prisma Postgres DevFeed: [Prisma Launches Startup Program With Credits and Support for Prisma Postgres]() Original publisher: [Read original article]() Author: Shane Neubauer Published: 2025-02-07T00:00:00Z Content type: release Language: en Sources: [Prisma]() Topics: [Prisma](), [Database](), [Object-relational mapping](), [Serverless](), [App](), [Slack]() Tags: [database](), [early-stage](), [founders](), [prisma](), [startups](), [support](), [workflows]() ### AI overview Prisma introduces a startup program for early-stage companies, offering database credits, infrastructure guidance, Slack support, and co-marketing opportunities. The program supports teams building scalable applications with Prisma Postgres and Prisma ORM. ### Source excerpt The Prisma Startups Program gives early-stage teams credits, guidance, and support to build faster with Prisma Postgres and scalable database workflows. ## Announcing the CodeRabbit Startup Program DevFeed: [Announcing the CodeRabbit Startup Program]() Original publisher: [Read original article]() Author: Aravind Putrevu Published: 2024-09-30T00:00:00Z Content type: release Language: en Sources: [CodeRabbit]() Topics: [ai code review](), [Code review](), [AI tools for developers](), [Code quality]() Tags: [ai-copilot](), [ai-powered](), [announcements](), [code-quality](), [code-review](), [code-reviews](), [code-security](), [cycles](), [developer-productivity](), [discord](), [early-stage](), [emerging-tech](), [english](), [half](), [startup](), [startup-program]() ### AI overview CodeRabbit's Startup Program offers eligible early-stage startups 50% off a Pro Plus annual plan for six months, after the standard 14-day free trial, plus support and access to its Discord community. Eligibility is limited to VC- or accelerator-backed startups without an existing CodeRabbit account and with under $25 million in funding. Applicants fill out a form; CodeRabbit says it will review applications within two business days and email approval details. The article describes AI-assisted pull request reviews intended to flag issues, improve code quality and security, and streamline collaboration, but its claims about cutting bugs and review time in half are not accompanied by supporting evidence in the supplied text. ### Source excerpt In the startup world, speed and quality are often seen as tradeoffs; with AI-powered code reviews, you can have both. Startups and small dev teams face numerous challenges shipping code day-to-day while navigating tight deadlines and meeting user expectations with limited resources. ## How Daytona secured a $5M seed round from Upfront Ventures DevFeed: [How Daytona secured a $5M seed round from Upfront Ventures]() Original publisher: [Read original article]() Author: Ivan Burazin CEO; Co-Founder Published: 2024-07-01T00:00:00Z Content type: article Language: en Sources: [Dotfiles Insider]() Topics: [Finance](), [Web](), [Open Source]() Tags: [browser-based-ide](), [browser-ide](), [business](), [cloud-coding](), [cloud-dev-environment](), [cloud-development-environment](), [cloud-editor](), [cloud-ide](), [cloud-programming](), [co-founder](), [code-in-cloud](), [code-on-web](), [coding-environment](), [company](), [early-stage](), [funding](), [ide-editor-online](), [ide-in-browser](), [ide-online](), [online-code-editor](), [online-ide](), [online-programming](), [online-web-dev-ide](), [online-web-development-ide](), [sde](), [standardised-development-environment](), [stories-insights](), [web-based-coding](), [web-ide]() ### AI overview Daytona describes how it secured a $5 million seed investment from Upfront Ventures after building a relationship with the firm. The founders cite founder-market fit, timing, trust, open-source traction, and an organized fundraising process as factors in the deal. They plan to use the funding to simplify development environments. ### Source excerpt Our journey with Upfront, led by Kevin Zhang and Peter Zakin, the importance of timing, shared vision, and trust in securing funding. ## Resend named to Redpoint's InfraRed 100 DevFeed: [Resend named to Redpoint's InfraRed 100]() Original publisher: [Read original article]() Author: Zeno Rocha Published: 2024-06-19T00:00:00Z Content type: news Language: en Sources: [Resend]() Topics: [cloud-infrastructure](), [Cloud Architecture]() Tags: [cloud](), [company](), [early-stage](), [email](), [founders](), [langchain](), [modal](), [ollama](), [railway](), [redpoint-infrared-100](), [venture-capital](), [zed]() ### AI overview Resend says it was included in Redpoint's InfraRed 100, a list of private cloud infrastructure companies. The company notes its selection as the only communications platform on this year's list and says it plans to continue building email infrastructure. ### Source excerpt Celebrating the up-and-coming private companies in cloud infrastructure. ## Q&A: Discover what's new in the Mobile Payments SDK beta DevFeed: [Q&A: Discover what's new in the Mobile Payments SDK beta]() Original publisher: [Read original article]() Author: Priscilla Wu Published: 2024-06-06T15:00:00Z Content type: release Language: en Sources: [Square Corner]() Topics: [SDK](), [mobile-app-development](), [Documentation](), [Developer experience]() Tags: [about](), [accessibility](), [account](), [advice](), [agent](), [all](), [apis](), [beta](), [early-stage](), [experience](), [integration](), [learn](), [mobile](), [mobile-payments](), [payments](), [q-a](), [sdk](), [software](), [stage](), [testing](), [what-s-new]() ### AI overview Square's Mobile Payments SDK beta lets apps process in-person payments with Square hardware and adds features including Orders integration, delayed capture, app fees, Reader Management, and Gift Card acceptance. The excerpt says it is available to developers serving US sellers. KIOS reports using Orders integration for itemized receipts that flow into Square systems, Reader Management for device status and remote troubleshooting, app fees for monetization, and offline payments to reduce reliance on connectivity. KIOS found integration manageable and documentation improved from its early alpha; its advice is to read the documentation and ask questions through Square's support channels. Offline payments submit automatically after reconnection and are declined if the device is not reconnected within 24 hours of the first offline payment; sellers bear responsibility for expired, declined, or disputed offline transactions. ### Source excerpt Learn about how KIOS, a kiosk software, conducted early stage testing of the SDK and their integration experience. ## GitHub Fund 2024 and beyond: Looking to the future DevFeed: [GitHub Fund 2024 and beyond: Looking to the future]() Original publisher: [Read original article]() Author: Stormy Peters Published: 2024-01-25T20:36:35Z Content type: news Language: en Sources: [GitHub Blog]() Topics: [GitHub](), [rachels-ramblings](), [GitHub Copilot]() Tags: [code](), [company-news](), [developer](), [developer-tools](), [early-stage](), [future](), [github](), [news-insights]() ### AI overview GitHub Fund describes its first year of investments in open-source startups and its plans for 2024. The fund says it invested in 10 companies working in areas including developer tools, AI, security, cloud infrastructure, and open-source analytics, and expects to invest in 8-10 more startups focused on AI, security, infrastructure, and DevOps. ### Source excerpt Celebrate the first year of GitHub Fund, our first investments, and a brief look of where we're going. [Next page]()