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aws2 min readCurated summary

Transform live video for mobile audiences with AWS Elemental Inference | Amazon Web Services

AWS Elemental Inference is a fully managed AI service that transforms landscape live and on-demand video into mobile-ready vertical formats and automatically generates clips. It works in real time alongside AWS Elemental MediaLive, reducing 6–10 seconds of latency compared with minutes of traditional postproduction. AWS aims to help broadcasters publish content to TikTok, Instagram Reels, YouTube Shorts, and similar platforms without manual editing or specialized AI expertise. ## Mobile-Optimized Video Transformation - Smart Crop reformats landscape broadcasts into a 9:16 vertical format. - AI tracks subjects and keeps important action visible while preserving broadcast quality. - The service can process live content as it is being broadcast, helping publishers capture viral moments quickly. - Clip generation identifies notable events—such as game-winning plays in soccer or basketball—and produces clips for rapid distribution. ## Deployment and Workflow Integration - Users can create and manage feeds through the standalone AWS Elemental Inference console. - A feed contains feature configurations and moves from `CREATING` to `AVAILABLE`. - Outputs can be configured for vertical cropping or clipping; clip outputs require a name, the `Clipping` type, and an `ENABLED` status. - AWS Elemental Inference can also be enabled directly in existing AWS Elemental MediaLive channels without changing the surrounding video architecture. - MediaLive includes an AWS Elemental Inference tab showing the service ARN, data endpoints, feed outputs, enabled features, and operational status. ## Real-Time Agentic AI Processing - The service analyzes video continuously and independently performs cropping and clip-generation workflows. - Its agentic AI operates without human prompting or manual intervention. - Multiple AI features run in parallel against the same stream through a “process once, optimize everywhere” model. - Fully managed foundation models are automatically updated and optimized, removing the need for dedicated AI infrastructure or specialist teams. - Processing latency is approximately 6–10 seconds. ## Availability and Pricing - AWS Elemental Inference is initially available in: - US East (N. Virginia) - US West (Oregon) - Europe (Ireland) - Asia Pacific (Mumbai) - It can be accessed through the MediaLive console or MediaLive APIs. - Consumption-based pricing charges for the features used and video processed, with no upfront commitment. - AWS plans additional capabilities and tighter integration with other Elemental services, including features aimed at video monetization. AWS Elemental Inference is best suited to broadcasters and streamers that need to repurpose live content for mobile platforms quickly. Organizations already using MediaLive can add automated cropping and clip generation with minimal architectural change.

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AIAI AgentsFoundation ModelsLive Streaming+4
airbnb4 min readCurated summary

Academic Publications & Airbnb Tech: 2025 Year in Review

Airbnb’s 2025 research program expanded across major academic venues, with a focus on applying AI, machine learning, and data science to search, ranking, personalization, and marketplace optimization. The company strengthened its presence at KDD and CIKM while broadening into NLP, optimization, and measurement science. Its research emphasized practical systems that improve experimentation, retrieval, recommendations, ranking quality, and booking conversion. ## Research Expansion in 2025 - Airbnb presented research at established conferences including KDD and CIKM, while expanding into venues such as COLING, LION, and VLDB. - Researchers used these conferences to: - Share production-scale findings with academic and industry peers. - Develop new collaborations. - Learn about emerging methods. - Mentor early-career researchers. - The work was organized around themes including applied machine learning for search and personalization, and NLP and LLM systems in production. ## Search Ranking and Marketplace Retrieval at KDD KDD is a leading conference for data mining, knowledge discovery, and large-scale analytics. Airbnb has presented there since 2018, and its 2025 contributions focused on improving search experimentation and location retrieval. - **Interleaving and counterfactual evaluation** - Airbnb introduced techniques for evaluating search-ranking ideas before launching full A/B tests. - These methods help teams identify promising experiments more quickly. - They are especially useful for accommodation booking, where long conversion cycles can make statistical significance slow to achieve. - The goal is to accelerate experimentation without compromising evaluation accuracy. - **Extreme classification for audience expansion** - Airbnb presented a high-precision approach to retrieving relevant geographic areas in its two-sided marketplace. - The system uses categorical location cells to identify areas containing listings a guest might realistically book. - This helps balance Airbnb’s diverse global inventory with users’ preferences for location, amenities, style, and price. - Airbnb also presented work on **TSMO**, or Two-sided Marketplace Optimization, and indicated that some technologies might eventually be open-sourced. ## Search and Recommendation Advances at CIKM At CIKM 2025, Airbnb’s Relevance and Personalization team had five peer-reviewed papers accepted. The work addressed search, recommendations, ranking metrics, multimodal representations, and user comparison behavior. - **Recommendations for overly narrow searches** - Airbnb developed a system that suggests alternatives when a guest’s search returns too few accommodations. - Suggestions may include different dates, broader amenity requirements, or adjusted price ranges. - The system aims to reduce search frustration and increase booking rates. - **Map-specific ranking optimization** - Maps account for approximately 80% of Airbnb search interactions. - Traditional feed-ranking assumptions do not accurately represent how users view and interact with map results. - Airbnb introduced a map-specific version of NDCG, a ranking-quality metric. - Experiments showed that optimizing for this metric improved bookings. - **BiListing multimodal embeddings** - BiListing combines listing text and photos into unified embedding representations. - It uses large language models and pretrained language-image models as ranking signals. - The approach produced a reported 0.425% improvement in normalized discounted booking gain and generated tens of millions of dollars in incremental revenue. - **Beyond pairwise learning-to-rank** - Airbnb proposed a learning-to-rank method that models interactions between items during pairwise comparisons. - This provides a more realistic view of what users prefer when evaluating search results. - The paper also describes efficient implementation techniques and online and offline evaluation results. - **Learning to Comparison-Shop** - The LTCS system models how users compare multiple listings rather than evaluating each listing independently. - It produced statistically significant improvements of 1.7% in NDCG and 0.6% in booking conversion rate. - The work reflects Airbnb’s broader effort to make ranking models sensitive to the context of an entire results page. ## NLP and Production LLM Systems - Airbnb also highlighted NLP and production LLM research, including participation in EMNLP. - Relevant application areas include: - Customer support. - Search and discovery. - Trust and safety. - EMNLP covers language-model architectures, training strategies, safety, evaluation, datasets, and open-source tooling. Airbnb’s 2025 publications show a strong emphasis on research that translates directly into marketplace performance. The most practical opportunities involve faster experimentation, context-aware ranking, multimodal listing understanding, and recommendation systems that help guests recover from overly restrictive searches.

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Large Language ModelsMachine LearningNatural Language ProcessingData Science+4
pinterest3 min readCurated summary

Piqama: Pinterest Quota Management Ecosystem

Piqama is Pinterest’s generic quota management ecosystem for controlling physical resources, service limits, and application-specific capacity. It centralizes quota definition, validation, authorization, distribution, enforcement, usage tracking, and optimization while allowing individual applications to customize implementation details. Its integrations demonstrate how the same platform can support both capacity management for Big Data and rate limiting for online services. ## Platform Architecture - Provides a centralized management portal accessible through REST and Thrift. - Supports multiple quota types and platforms. - Applications may use Piqama’s default enforcement mechanisms or supply their own. - Manages quotas throughout their lifecycle, from creation and updates to usage feedback and optimization. ## Quota Lifecycle Management - **Schema management:** Defines quota identifiers and hierarchical relationships, such as workloads within projects. - **Validation:** Supports pluggable schema and semantic validation, including remote checks to ensure quotas do not exceed cluster capacity. - **Authorization:** Requires ownership-based authorization for quota updates and deletions; owners may be individuals or groups. - **Update dispatch:** Can distribute changes through Piqama clients, Pinterest’s PinConf system, or custom dispatchers. - **Enforcement:** Default clients can make real-time decisions, such as serving or dropping requests when usage exceeds limits. - Applications can customize schema handling, validation, update delivery, and enforcement logic. ## Governance and Auto-Rightsizing - Piqama clients collect quota enforcement and usage statistics transparently. - Non-client applications can submit data through system-based or storage-based feedback loops. - Data is stored in Apache Iceberg on Amazon S3 using predefined schemas and pre-aggregation to reduce storage costs. - An independent rightsizing service consumes historical data from Presto, Iceberg, and other sources. - Rightsizing strategies account for organic growth, traffic bursts, and underutilization. - Pinterest has developed a capacity-quota strategy intended to maximize resource allocation without saturating Big Data systems. ## Quotas and Budgets - Budgets assign dollar amounts to organizations, teams, or projects, while quotas define the resources available within those financial constraints. - Chargeback systems convert resource consumption into costs. - Projects that exceed their budgets may receive reduced resource allocations based on their tier. - Teams may need additional funding or workload prioritization when resources are restricted. - Piqama is expected to integrate further with Pinterest’s Entitlement system. ## Capacity-Based Quotas in Big Data - Pinterest’s Moka platform uses Apache YuniKorn to schedule batch-processing resources such as memory, CPU, and GPUs. - Piqama manages project-level quotas including: - Guaranteed memory and vcore allocations. - Maximum memory and vcore consumption. - Maximum concurrent applications. - Quota values are generated through: - **Auto-rightsizing:** Uses historical usage within a sliding window to estimate future needs. - **Manual adjustments:** Allows development teams to make immediate quota changes. - Pinterest is also developing a budget-based method for generating quota values. Piqama provides a flexible foundation for governing resource consumption across Pinterest. Organizations adopting it can combine centralized policy and visibility with application-specific enforcement, while usage data enables more efficient and financially aligned quota allocation.

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Amazon S3Apache IcebergRate LimitingResource Scheduling+4
grammarly3 min readCurated summary

10 Writing Strategies to Become a Better Writer

Writing strategies are deliberate methods for planning, drafting, and revising more effectively. They help writers turn ideas into clear, organized, and consistent work across academic, professional, and creative contexts. The article emphasizes that strategies are flexible habits rather than rigid rules, and that writers should adapt them to their purpose, audience, and deadlines. ## What Writing Strategies Are - Writing strategies guide decisions throughout the writing process, from brainstorming to revision. - Different stages call for different approaches: - Freewriting and clarifying goals help generate ideas. - Bullet-point outlines organize information. - One main idea per paragraph improves coherence. - Peer feedback supports revision. - Strategies differ from writing techniques: strategies concern the writer’s approach, while techniques concern how sentences and rhetoric are crafted. - The same strategies can apply to essays, reports, proposals, emails, research papers, and creative work. ## Why Writing Strategies Matter - Replace guesswork with intentional planning and revision. - Make writer’s block easier to overcome by providing concrete next steps. - Improve clarity, readability, and organization. - Reduce rambling, unclear arguments, and overly complicated explanations. - Save time by improving existing drafts instead of repeatedly starting over. - Build confidence and consistency across different writing tasks. - Help writers adapt their tone and structure to different audiences and goals. ## Strategies for More Effective Writing - **Clarify the purpose:** Decide whether the piece should explain, persuade, analyze, or achieve another goal. A one-sentence purpose statement helps determine what belongs in the draft. - **Break the task into smaller parts:** Divide large projects into manageable sections, such as drafting the introduction, body paragraphs, and conclusion in separate sessions. - **Outline before drafting:** Use brief notes or bullet points to organize major ideas, identify gaps, and avoid repetition. - **Write a rough draft:** Prioritize getting ideas on the page rather than perfect grammar or wording. Editing can happen later. - **Use one main idea per paragraph:** Develop a single claim or point, explain it, and support it with an example or evidence. - **Use concrete examples:** Pair abstract claims with specific details, scenarios, or statistics to improve credibility and understanding. - **Revise for clarity first:** Address meaning, organization, and confusing passages before polishing style, tone, or word choice. ## Practical Application Writers should experiment with different strategies and combine them according to the project. For example, they might clarify their purpose, outline key points, produce an imperfect draft, and then revise the structure before editing individual sentences. With repeated practice, these approaches become habits that make writing more manageable and polished.

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Writing StrategiesEffective WritingDrafting TechniquesClarity Improvement+3
github2 min readCurated summary

Multi-agent workflows often fail. Here’s how to engineer ones that don’t.

Multi-agent workflows often fail because agents make implicit assumptions about state, ordering, and intended actions. The post argues that these systems should be engineered like distributed software rather than treated as chat interfaces. Typed schemas, explicit action definitions, and MCP-enforced interfaces make agent behavior more predictable and failures easier to contain. ## Typed Schemas Prevent Data Drift - Natural-language exchanges and inconsistent JSON lead to changing field names, mismatched types, and ambiguous payloads. - Typed interfaces define machine-checkable contracts, such as a `UserProfile` with fixed fields and allowed plan values. - Schema violations can fail fast, triggering retries, repairs, or escalation before invalid state spreads. - Debugging becomes contract-based instead of dependent on inspecting logs and guessing. ## Action Schemas Clarify Intent - Agents cannot reliably infer what “take action” means; they may assign, close, escalate, or do nothing. - Action schemas restrict outcomes to explicit, valid choices such as: - Requesting more information - Assigning an issue - Closing an issue as a duplicate - Taking no action - A discriminated union or similar structure ensures every agent returns one recognized action. - Invalid or ambiguous actions can be rejected, retried, or escalated. ## MCP Enforces Agent Interfaces - Schemas and action definitions are only conventions unless consistently enforced. - Model Context Protocol (MCP) provides explicit input and output schemas for tools and resources. - Calls are validated before execution, preventing agents from inventing fields, omitting required inputs, or drifting between interfaces. - MCP therefore acts as the enforcement layer for both data structure and intended behavior. Reliable multi-agent systems require explicit contracts at every boundary. Engineers should treat agents like code components: define their data and actions precisely, enforce interfaces with mechanisms such as MCP, and prevent invalid state from propagating.

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AI AgentsModel Context ProtocolGithub CopilotDistributed Systems+2
netflix3 min readCurated summary

MediaFM: The Multimodal AI Foundation for Media Understanding at Netflix

Netflix’s Media Foundational Model (MediaFM) is a tri-modal AI system that combines video, audio, and timed text to understand long-form entertainment. It represents sequences of shots while using title-level metadata and temporal context to produce richer content embeddings. Netflix concludes that these contextual embeddings improve many downstream tasks, including advertising relevance, clip selection, tone classification, and popularity prediction. ## Motivation for MediaFM - Netflix needs machine-readable understanding of its expanding catalog, including films, series, live events, and podcasts. - Long-form media requires recognizing narrative dependencies, emotional arcs, scene transitions, and subtle tones across entire episodes or films. - Combining visual, audio, and textual signals provides a more complete understanding than relying on video alone. - The resulting embeddings support applications such as: - Cold-start recommendations for new titles - Promotional art and trailer optimization - Advertising relevance - Clip tagging and internal content analysis ## Multimodal Input Representation - The model uses a shot as its fundamental unit, with titles segmented using shot-boundary detection. - Each shot receives three modality-specific embeddings: - **Video:** Frames sampled from the shot are encoded with SeqCLIP, Netflix’s video-retrieval model. - **Audio:** Sound is encoded using Meta FAIR’s wav2vec2. - **Timed text:** Captions, subtitles, or audio descriptions are encoded with OpenAI’s `text-embedding-3-large`. - The three embeddings are concatenated and unit-normalized into a 2,304-dimensional fused vector. - Training examples consist of temporally ordered shot sequences from a movie or episode, with up to 512 shots. - Title metadata, such as synopses and tags, is also embedded and supplied as global context. ## Transformer Architecture - MediaFM uses a BERT-like Transformer encoder. - Fused shot embeddings are first projected into the model’s hidden dimension. - Two special tokens are prepended: - `[CLS]`, a learnable sequence-level embedding - `[GLOBAL]`, containing projected title-level metadata - Positional embeddings and self-attention allow each shot representation to incorporate surrounding narrative context. - A final projection maps contextualized representations back into the original 2,304-dimensional embedding space. ## Masked Shot Modeling - The model masks 20% of shot embeddings in each training sequence. - Masked inputs are replaced with a learnable `[MASK]` embedding. - The Transformer must reconstruct the original fused embedding for each masked shot. - Training minimizes cosine distance between predicted and ground-truth embeddings. - Hidden parameters are optimized with Muon, while other parameters use AdamW; Netflix reports noticeable gains after adopting Muon. ## Evaluation Through Linear Probes - Netflix evaluates MediaFM by freezing its representations and training task-specific linear layers on top. - Most evaluation tasks involve short clips extracted from larger titles. - Embedding a clip within the context of its surrounding episode or film performs better than embedding the clip in isolation, demonstrating the value of long-range contextualization. ## Downstream Applications - **Ad relevancy:** Multilabel classification identifies clips suitable for relevant advertising; MediaFM helps retrieve candidate clips before ad-serving optimization. - **Clip popularity ranking:** The model predicts relative clip performance and click-through rate within a title, evaluated using Kendall’s tau. - **Clip tone:** Clips are classified into 100 categories, such as creepy, scary, or humorous. - **Clip genre:** Clips are assigned to core genres including Action, Comedy, Documentary, Drama, Horror, Romance, and Thriller. - **Clip retrieval:** The system distinguishes “clip-worthy” content from unsuitable clips based on human annotations, using Average Precision. MediaFM’s main practical lesson is that effective media understanding depends on fusing all available modalities and preserving long-form temporal context. Netflix’s approach provides a reusable embedding foundation for recommendation, promotion, advertising, and content-analysis systems rather than building a separate representation for every task.

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Database DesignTransformerMultimodalVideo Embedding+4
discord3 min readCurated summary

Getting Global Age Assurance Right: What We Got Wrong and What's Changing

Discord’s CTO says the company mishandled communication around its global age-assurance rollout, leading users to believe that face scans or ID uploads would be required for everyone. Discord’s stated goal is to protect teens and restrict age-sensitive content while preserving the normal experience for most users and avoiding collection of users’ identities. The company plans to use internal signals for most age determinations and privacy-focused third-party verification only when necessary. ## Where Discord Says It Fell Short - Discord did not clearly explain how age assurance would work or who would be affected. - Many users mistakenly believed that everyone would need to submit a face scan or government ID. - The company acknowledges that skepticism about technology companies collecting personal data is justified. - Discord says the rollout was not intended to create a pretext for gathering more personal information. ## The Goal of Age-Appropriate Experiences - More than 90% of users are expected to continue using Discord without verifying their age. - Discord wants teenagers to receive stronger safeguards while allowing adults to access the full range of content. - The approach is being influenced by laws already taking effect in the UK and Australia, with Brazil, Europe, and several US states following. - Discord says building its own system could demonstrate that age can be verified without identifying users. ## Internal Age Determination - Discord’s systems may determine age using account-level signals, including: - Account age - Whether a payment method is attached - Server membership patterns - General account activity - The system will not read private messages, analyze conversations, or inspect posted content. - Discord compares this process to existing safety systems used to detect spam, raids, and coordinated abuse. - The company plans to publish its methodology before the global launch. ## What Happens If Verification Is Required - Fewer than 10% of users are expected to need additional verification. - Users who do not verify can keep: - Their accounts - Servers - Friends lists - Direct messages - Voice chat access - They will only lose access to age-restricted content and the ability to change certain default safety settings intended to protect teens. - A user’s age group will remain private and will not be visible to other Discord users. ## Third-Party Verification Partners - Discord plans to use vendors when its internal systems cannot confirm that a user is an adult. - Vendors are intended to return only an age group, not the user’s identity. - Discord says vendors cannot connect verification information back to a Discord account, and Discord cannot use the process to learn the user’s identity. - Partners undergo security and privacy reviews, contractual data-use restrictions, and retention and deletion requirements. - Verification data is generally deleted immediately after it is no longer needed. - Discord clarified that the vendor involved in a previous customer-service security incident is not used for age assurance and is no longer a partner. - The company tested Persona in the UK in January but decided not to proceed with that vendor. Discord’s revised approach emphasizes minimal data collection, multiple verification choices, and transparency about how age estimation works. Publishing the methodology and clearly explaining vendor safeguards will be essential to rebuilding user trust.

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Machine LearningIdentity VerificationData ProtectionPrivacy+3
gitlab3 min readCurated summary

Agentic SDLC: GitLab and TCS deliver Intelligent Orchestration across the enterprise

GitLab and TCS are partnering to help enterprises scale DevSecOps through AI-agent orchestration. Their combined approach addresses fragmented toolchains, inconsistent security, manual compliance, and the risks of AI-generated code by combining GitLab’s unified platform and guardrails with TCS’s migration, architecture, and industry expertise. The goal is to evolve DevSecOps into auditable “Intelligent Orchestration,” where humans and AI agents collaborate across the software lifecycle. ## Supporting the Future-Ready Enterprise - GitLab’s unified data model connects planning, coding, testing, security, and deployment in one source of context. - Enterprises can standardize pipelines, controls, and metrics without repeatedly re-engineering their development platforms. - GitLab and TCS combine: - Multi-agent orchestration - Dynamic planning - Confidence-scored decisions - Continuous learning cycles - GitLab Duo agents, including Planner, Security Analyst, and Code Review agents, can be invoked through MCP-driven integrations and TCS’s structured agent hierarchy. - These agents operate with project context while remaining subject to GitLab’s AI-native DevSecOps controls. ## Scaling DevSecOps Through Platform Engineering - Platform engineering replaces individually managed pipelines with an Internal Developer Platform (IDP). - Self-service “golden paths” standardize how applications are built, tested, secured, and deployed. - Policy-as-code embeds governance, compliance, and security into development by default. - GitLab serves as the IDP control plane. - TCS designs and industrializes self-service workflows around that control plane. - GitLab Duo adds AI-driven automation to development and operational tasks. ## From DevSecOps to Intelligent Orchestration - Traditional DevSecOps platforms manage code, pipelines, and controls; intelligent orchestration also coordinates human and AI work. - GitLab Duo agents can work in parallel on: - Code generation - Testing - Code review - Security analysis - CI/CD troubleshooting - Pipeline repair - Developers remain in control through rules and guidance while agents handle repetitive or multi-step work. - Agent actions are contextual, auditable, and policy-aligned. - This allows organizations to extend AI across thousands of engineers while preserving security and regulatory compliance. ## The GitLab–TCS Reference Architecture - GitLab contributes: - Intelligent Orchestration across the DevSecOps lifecycle - Unified project context - Specialized AI agents - Integrated security and compliance controls - TCS contributes: - Reference architectures - Migration factories - Enterprise security baselines - AI capabilities and risk-management frameworks - Platform engineering and adoption programs - TCS’s industry and regulatory experience helps adapt GitLab to legacy systems, organizational models, compliance obligations, and multi-cloud environments. - The partnership emphasizes enterprise-scale adoption rather than isolated tooling deployments. Together, GitLab and TCS recommend building an Internal Developer Platform with embedded policy-as-code, self-service workflows, and governed AI agents. This approach can reduce delivery friction and manual toil while allowing enterprises to scale automation without sacrificing oversight, security, or compliance.

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Machine LearningCI/CDGitlabDevSecOps+4
gitlab2 min readCurated summary

GPG key used to sign GitLab package repositories' metadata has been extended

GitLab has extended the expiration date of the GPG key used to sign metadata for its APT and YUM repositories. The key, fingerprint `F640 3F65 44A3 8863 DAA0 B6E0 3F01 618A 5131 2F3F`, now expires on February 6, 2028, rather than February 27, 2026. Existing users may need to refresh the key, while new users can follow the standard GitLab installation instructions. ## Purpose of the Signing Key - GitLab uses GPG signatures to protect repository metadata for: - `omnibus-gitlab` packages - `gitlab-runner` packages - Repository metadata signing is separate from package signing. - The key’s expiration is periodically extended to follow security policies and reduce exposure if it is compromised. ## Required Actions - Users who configured GitLab repositories before February 17, 2026, should follow GitLab’s documentation to fetch and install the updated key. - New users do not need special steps beyond following the GitLab or GitLab Runner installation guides. - The public key can be retrieved: - From GPG keyservers using the fingerprint or associated email address - Directly at `https://packages.gitlab.com/gpg.key` ## Getting Help - Documentation explains how to verify repository metadata signatures. - Additional issues should be reported in the `omnibus-gitlab` issue tracker. Users with existing GitLab package repositories should refresh their trusted GPG key to avoid future signature-validation problems.

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GitlabDevSecOpsGpgSecurity Policies+2
aws3 min readCurated summary

AWS Weekly Roundup: Claude Sonnet 4.6 in Amazon Bedrock, Kiro in GovCloud Regions, new Agent Plugins, and more (February 23, 2026) | Amazon Web Services

AWS’s February 23, 2026 roundup highlights continued investment in AI-assisted development, cloud infrastructure, and production-grade agents. Major launches include Claude Sonnet 4.6 in Amazon Bedrock, Kiro for GovCloud, customizable SageMaker deployments for Nova models, and new EC2 and Aurora capabilities. The post also points developers toward agent tooling, operational best practices, community resources, and upcoming events. ## Developer Conferences and AI Collaboration - AWS teams discussed “renascent software,” where humans and AI work together as co-developers through Kiro. - Developer Week sessions focused on: - Agent memory - Multi-agent architectures - Meta-tooling - Hooks - Production deployment of AI agents - At dev/nexus in Atlanta, AWS speakers will cover AI agents with Spring and MCP, along with AI-assisted Java modernization. ## Major AWS Launches - **Claude Sonnet 4.6 in Amazon Bedrock** - Provides near-Opus 4.6 intelligence at lower cost. - Targets coding, agent workloads, and professional knowledge work. - Designed for fast, high-quality task completion at scale. - **Amazon EC2 Hpc8a instances** - Powered by 5th Gen AMD EPYC processors. - Deliver up to 40% higher performance, increased memory bandwidth, and 300 Gbps Elastic Fabric Adapter networking. - Intended for simulations, engineering, and tightly coupled HPC workloads. - **Custom Amazon Nova models with SageMaker Inference** - Supports configuration of instance types, auto-scaling policies, and concurrency. - Enables deployments to be tuned for specific performance and cost requirements. - **Nested virtualization on EC2** - Allows KVM or Hyper-V virtual machines to run inside virtual EC2 instances. - Supports mobile emulators, automotive hardware simulation, and Windows Subsystem for Linux environments. - **Aurora encryption by default** - New database clusters automatically use server-side encryption with AWS-owned keys. - Encryption is transparent, fully managed, and has no additional cost or performance impact. - **Kiro in AWS GovCloud** - Brings Kiro’s agentic development capabilities to teams working on government missions. - Supports regulated environments requiring stringent security controls. ## Agent Tools and Operational Reliability - AWS introduced open-source **Agent Plugins for AWS** that add AWS-specific skills to coding agents. - The `deploy-on-aws` plugin can generate: - Architecture recommendations - Cost estimates - Infrastructure-as-code - AWS also highlighted automated reasoning research led by Byron Cook, applying formal verification techniques to AI-generated code and critical agent decisions. - Recommended practices for AWS DevOps Agent focus on configuring Agent Spaces to balance broad investigation capabilities with operational efficiency. - AWS reports that DevOps Agent has handled thousands of escalations and achieved an estimated root-cause identification rate above 86% within Amazon. ## Community Projects and Resources - Community content includes: - A practical guide to AWS for developers entering their first job. - An AI agent that automates job searching. - A Kiro Power integrating 25 MCP tools, 10 steering guides, and structured development guidance. - AWS encourages developers to use the AWS Builder Center to exchange knowledge and discover community content. ## Upcoming Events and Hackathons - 2026 AWS Summits are scheduled for Paris, London, and Bengaluru. - The six-week Amazon Nova AI Hackathon runs through March 16, with $40,000 in prizes across areas such as agentic AI, multimodal applications, UI automation, and voice. - Upcoming AWS Community Days include events in Ahmedabad, Tokyo, Chennai, Slovakia, and Pune. Developers interested in AI-assisted coding, agent operations, or high-performance cloud workloads should explore the new Bedrock, Kiro, SageMaker, and Agent Plugin capabilities, while using AWS events and community forums for practical guidance.

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AI AgentsGenerative AIAmazon BedrockAmazon Ec2+4
grammarly2 min readCurated summary

Superhuman Go Scales Agent Ecosystem With New Partner Agents From Box, Gamma, and Wayground

Superhuman Go expands Grammarly’s workflow assistant with agents that connect enterprise knowledge, create visual content, support learning, and improve communication. These integrations let users work directly from their existing context instead of switching between tools, while keeping tasks such as document reuse, presentation creation, research, and feedback in one workflow. ## Enterprise Knowledge and Workflow Automation - **Box** connects document repositories to Go, allowing users to: - Create Box documents in the appropriate folders. - Search existing files for summaries, extracted information, and reusable knowledge. - Find the latest document versions while keeping Box as the source of truth. - **Common Room** brings buyer intelligence from multiple channels into users’ workflows. - **Fireflies** surfaces meeting summaries, action items, and key decisions to speed up follow-up. - **Parallel** checks facts, recommends citations, and adds real-time data for more credible work. - **Latimer** combines internal search with bias detection to support precise, fair writing. ## Visual Content Creation - **Gamma** turns notes, documents, and meeting recaps into polished, structured presentation decks. - **Napkin AI** converts written content into visual frameworks designed to improve clarity and drive action. ## Interactive Learning - **Wayground** creates quizzes and flashcards from content visible on screen, including emails, documents, slides, and web pages. - **Quizlet** transforms notes, essays, and other written materials into flashcards with a single prompt. - **Speechify** supports listening at speeds up to 4.5 times faster using AI voices designed to improve comprehension. ## Communication, Feedback, and Compliance - **Radical Candor®** helps users handle difficult feedback using Kim Scott’s framework. - **Saifr** assists financial organizations with clear, compliant public communications by detecting regulatory risks and suggesting safer language. ## Building Custom Agents - The Superhuman Agents SDK and MCP client allow organizations to build agents that operate across Go. - The SDK is currently in private beta, with applications available for organizations interested in developing their own agents. Superhuman Go is available to Grammarly users through its Chrome and Edge browser extensions, with Mac and Windows support planned. Together, the integrations position Go as a central workspace for turning existing information into documents, presentations, learning materials, research, and compliant communications without constant tool switching.

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AIGoSdkInteractive Learning+4
cloudflare3 min readCurated summary

Cloudflare One is the first SASE offering modern post-quantum encryption across the full platform

Cloudflare says Cloudflare One is now the first SASE platform to provide standards-compliant post-quantum hybrid ML-KEM encryption across Secure Web Gateway, Zero Trust, and WAN connectivity. The update extends protection to Cloudflare IPsec and Cloudflare One Appliance, addressing both current “harvest now, decrypt later” attacks and future quantum threats. The appliance support is generally available in version 2026.2.0, while Cloudflare IPsec is in closed beta. ## Post-Quantum Cryptography Is an Immediate Concern - NIST has set 2030 as the target for phasing out RSA and elliptic-curve cryptography in favor of post-quantum algorithms. - Cryptographic migrations can take decades, as demonstrated by vulnerabilities involving deprecated algorithms such as MD5. - Organizations face “harvest now, decrypt later” attacks, in which encrypted traffic is collected today for future decryption. - Cloudflare argues that built-in crypto agility makes it easier for enterprises to upgrade algorithms without redesigning remote-access and WAN infrastructure. ## Two Required Cryptographic Migrations - **Key establishment:** ML-KEM is becoming the standard post-quantum mechanism for establishing shared encryption keys. - Cloudflare uses hybrid ML-KEM, combining ML-KEM with classical ECDHE. - This approach protects against harvested traffic, requires no specialized hardware like quantum key distribution, and has limited performance impact. - More than 60% of human-generated TLS traffic reaching Cloudflare is already protected by hybrid ML-KEM. - **Digital signatures:** Post-quantum signatures protect against server impersonation but are larger than current ECC signatures. - Their migration is considered less urgent because they primarily defend against active quantum adversaries, which do not yet exist. - Cloudflare’s current IPsec work therefore focuses on post-quantum key establishment rather than signatures. ## Post-Quantum Protection for Cloudflare IPsec - Cloudflare upgraded its IPsec products to support hybrid ML-KEM within IKEv2. - Cloudflare IPsec creates encrypted tunnels from customer networks to Cloudflare’s global network. - IP Anycast routes tunnels to the nearest data center and automatically redirects traffic if a location becomes unavailable. - The service supports site-to-site WAN connectivity as well as outbound Internet connections. - Cloudflare One Appliance, which establishes Cloudflare IPsec connections, supports the upgrade starting with version 2026.2.0. - The Cloudflare IPsec upgrade remains in closed beta. ## Limitations of Earlier IPsec Approaches - IPsec has historically evolved differently from TLS because it is commonly used between devices from the same vendor, making interoperability less central. - RFC 8784 proposed combining long-lived pre-shared keys with Diffie-Hellman exchange. - While this can help protect against harvest-now-decrypt-later attacks, it does not provide forward secrecy against quantum attackers. - Quantum key distribution is also impractical for many enterprise environments because it requires specialized physical connectivity. Cloudflare’s recommendation is to begin post-quantum migration now, starting with hybrid ML-KEM for key establishment across Internet access, Zero Trust, and WAN connections.

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Zero TrustNetwork SecurityPost Quantum CryptographySase+4
daangn3 min readCurated summary

Improving the iOS WebView Input Experience for

iOS WebViews can shift the entire page upward when an input receives focus, disrupting users as the virtual keyboard appears. The post describes four progressively refined approaches, ultimately using a single input whose opacity is temporarily set to zero before focus. This avoids iOS’s automatic scroll behavior, though it remains a fragile, version-dependent workaround best reserved for input-critical screens. ## Understanding the iOS WebView Problem - iOS uses two viewport concepts: - **Layout Viewport:** The CSS layout area, which does not shrink when the keyboard opens. - **Visual Viewport:** The area visible to the user, which becomes smaller. - Because the focused input must remain visible above the keyboard, iOS shifts the page upward. - The resulting movement can push important content off-screen, and WebView code cannot fully control this behavior. ## Attempt 1: Restore the Scroll Position - The first approach listened for `visualViewport` `resize` and `scroll` events. - It reduced the wrapper’s height by the keyboard height and called `window.scrollTo(0, 0)`. - This failed because iOS moved the page before the event handler could restore it. - Users saw flickering and shaking as multiple resize events triggered repeated corrections. ## Attempt 2: Follow `offsetTop` - Instead of undoing the movement, the implementation followed it. - `visualViewport.offsetTop` was used to adjust a fixed wrapper’s `top` position. - A short delay was required because iOS updates `offsetTop` asynchronously. - This reduced the visible jump but still caused subtle shaking while the keyboard animated. - Rapidly switching inputs or opening and closing the keyboard made the instability more noticeable. ## Attempt 3: Fake and Real Input Swap - This approach prevented iOS from scrolling to the focused input by focusing a real input positioned off-screen. - A visible, read-only “fake” input received the user’s tap, then focused the hidden real input. - Once the keyboard appeared, the two inputs were swapped so the real input became visible. - The page no longer experienced major upward movement. - However, two inputs required synchronization of values, selection, placeholders, textarea sizing, mentions, and emojis. - Swap timing, keyboard detection, and native bridge dependencies made the implementation increasingly complex. ## Attempt 4: Temporarily Hide the Input with Opacity - The final approach kept only one input. - On `touchstart`, the input’s `opacity` was set to `0` before calling `focus()`. - iOS appeared not to perform its automatic scroll-into-view behavior for an invisible input. - After detecting that the keyboard had opened—using the visual viewport and a keyboard-height threshold—the input’s opacity was restored to `1`. - This preserved the input’s state and avoided synchronization problems. - The technique is still a hack: behavior may change across iOS versions, and it can cause minor timing issues. The recommended compromise is to apply the opacity workaround selectively on pages where writing is central to the experience, rather than across the entire WebView application. It prioritizes uninterrupted user interaction despite the lack of a clean, standard iOS API.

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User ExperienceiOSWeb DevelopmentWebview+4
line4 min readCurated summary

Sharing the journey of LINE DEV

AI adoption at LY Corporation has moved beyond experimentation toward learning how to use these tools effectively in real work. The LINE DEV AI Reporters program connects scattered individual and team experiences through internal sharing sessions, helping practical lessons spread across the organization. Its central conclusion is that AI productivity depends not only on tools, but also on clear specifications, sound engineering practices, and a culture of continuous sharing. ## Turning Individual Experiments into Organizational Knowledge - AI enthusiasts across LY Corporation were independently experimenting with tools such as ChatGPT and Claude Code. - These experiences often remained limited to individuals or small teams. - AI Reporters brought together members of different roles and seniority who had experience sharing AI-related work. - Their goal was to turn personal trial and error into reusable organizational knowledge. ## Starting with Informal Personal Experiments - Early AI sharing sessions emphasized accessibility rather than polished success stories. - Multimedia Platform Dev’s Choi Jeong-min shared a “one service a day” vibe-coding experiment using Claude Code and Antigravity. - The experiment demonstrated that rapid implementation increases the importance of clearly defining what to build. - Sharing failures and unfinished experiments reduced the pressure to perform and encouraged more employees to try AI themselves. ## Applying AI to Real Development Work - As interest grew, discussions shifted from fun experiments to practical workplace applications. - Data Dev4’s Lee Yun-seong shared more than a month of project experience using Claude Code, project templates, and Vibe Kanban. - Developers spent more time on planning, design, review, and coordination while agents handled implementation. - Because the current codebase becomes the context for future agent work, poor architecture and coding styles can quickly be reproduced and amplified. - Continuous testing, refactoring, documentation, interface management, and architectural cleanup are therefore essential. - Skipping automated tests before commits led to increasing numbers of broken changes during later merges. - Humans remain responsible for ensuring that AI-generated code actually contributes to the project. - The most valuable skills increasingly involve task design, project management, system context, and meta-programming rather than implementation alone. - Developers can work in parallel with agents by planning the next task, researching requirements, and reviewing completed code while agents execute current work. ## Expanding from Teams to Organization-Wide Programs - Fintech Engineering organized a hands-on workshop covering the full path from idea to deployment. - Participants connected ChatGPT, Claude Code, and Stitch AI to plan, design, build, and complete a working service. - The integrated workflow helped participants understand how AI tools can support an entire product-development process, not just prototyping. - The GAI Study Group in the advertising organization broadened discussions to AI strategy, trends, agent behavior, developer workflows, and business applications. - Topics included: - AI agent reliability - Implementing interactions between PyTorch-based LLMs and MCP servers - Senior and junior developers’ vibe-coding workflows - NotebookLM-based RAG using wiki pages and Slack conversations - One session examined MCP internals by implementing JSON-RPC messaging and session-state management directly, revealing complexities hidden by libraries such as FastMCP. - Sessions were opened to participants and presenters from other teams, with some content published online for wider access. ## Building a Culture of Continuous Sharing - The most useful AI knowledge came from real workplace attempts, failures, and revisions—not only from polished documentation or external trends. - AI Reporters made existing but scattered experiences visible and connected them through presentations, Slack discussions, and monthly meetings. - Informal conversations such as “I tried this—how did it work for you?” helped normalize experimentation and learning from mistakes. - AI adoption is treated as an ongoing practice because tools and workflows continue to change. LY Corporation’s experience suggests that organizations should create lightweight, recurring forums where employees can share practical AI experiments. The combination of rapid experimentation, disciplined engineering, and open knowledge exchange allows individual discoveries to become lasting organizational capability.

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AIPostgreSQLClaude CodeChatgpt+4
cloudflare3 min readCurated summary

Cloudflare outage on February 20, 2026

Cloudflare suffered a 6-hour, 7-minute outage on February 20, 2026, after a software change unintentionally withdrew Internet routes for some Bring Your Own IP (BYOIP) customers. The incident was not related to a cyberattack; a buggy automated cleanup task altered customer prefix and service configurations. Cloudflare reverted the change, restored affected prefixes, and is revising its Addressing API workflows to reduce production risk. ## Customer Impact - Approximately 1,100 of Cloudflare’s 6,500 advertised prefixes were withdrawn between 17:56 and 18:46 UTC. - This affected about 25% of the 4,306 BYOIP prefixes advertised globally. - Impacted applications became unreachable from the Internet and experienced connection failures and timeouts. - Customers initially encountered BGP Path Hunting, where networks repeatedly searched for a route until connections timed out. - The `one.one.one.one` website returned HTTP 403 errors and an “Edge IP Restricted” message. - DNS resolution through the 1.1.1.1 resolver, including DNS over HTTPS, was not affected. - The incident did not affect every BYOIP customer because the configuration change was applied incrementally and was reverted before reaching everyone. ## Recovery Efforts - Engineers detected the issue through failures involving `one.one.one.one` and reverted the change. - Cloudflare published dashboard guidance at 19:19 UTC, allowing many customers to re-advertise their prefixes themselves. - Around 800 prefixes were restored by approximately 20:20 UTC. - About 300 prefixes could not be restored through the dashboard because their service configurations had been removed from edge servers. - Engineers manually restored those remaining prefixes at 23:03 UTC. - Some customers continued to experience latency and failures while addressing configuration state propagated back to the edge. ## The Addressing API - Cloudflare’s Addressing API is the authoritative dataset for IP addresses present on its network. - Changes to the API drive workflows that propagate address and routing updates across Cloudflare’s edge. - The normal process is: - Customers request advertisement or withdrawal through the Addressing API or BGP Control. - The API instructs machines to change prefix advertisements. - Routers update BGP after enough machines receive the change. - Customers bind Cloudflare products to their BYOIP ranges. - Because the API is closely connected to production systems, manual changes are risky. - Cloudflare’s “Code Orange: Fail Small” initiative aims to replace manual Addressing API operations with safer, automated, health-checked workflows. ## Root Cause: Faulty BYOIP Cleanup Automation - The failed change automated the removal of prefixes from BYOIP, a task that had previously been performed manually. - A recurring cleanup sub-task searched for BYOIP prefixes marked for deletion and removed them. - The cleanup task issued the API request: ```go /v1/prefixes?pending_delete ``` - The request contained a bug in how the API query was interpreted. - As a result, the cleanup process unintentionally withdrew customer prefixes and removed related service configurations from some edge servers. - The incident lasted much longer than the initial withdrawal because restoring both advertisements and edge configuration state required extensive automated and manual recovery. Cloudflare’s main corrective direction is to make Addressing API changes safer through incremental, health-mediated deployment, stronger safeguards around automated deletion, and elimination of risky manual production workflows.

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GoAPI DesignDnsBgp+3