The provided Microsoft Engineering blog URL no longer contains the article “Scaling AI for Silicon.” Instead, it returns a 404 error page, so the post’s argument, technical details, and conclusion cannot be determined from the supplied content.
## Missing Article
- The requested URL points to `devblogs.microsoft.com/engineering-at-microsoft/scaling-ai-for-silicon/`.
- Microsoft reports: “Oops, 404 Error! That page can’t be found.”
- No article text, author, publication date, or technical discussion is included.
## Available Resources
- Links are provided to Microsoft Docs, Visual Studio, Microsoft Learn, Developer Community, and the Dev Blogs FAQ.
- The page also lists Microsoft’s other technology blogs, including Azure, .NET, AI, engineering, and Windows-related blogs.
- A link to the Engineering@Microsoft blog homepage is available, but the requested post itself is not.
The article cannot be accurately summarized without its original text or an accessible replacement URL.
Solo founders now represent 63% of new Stripe Atlas C corps, but performance is increasingly polarized: median revenue is falling while top performers grow rapidly. Stripe’s analysis of thousands of solo-founded startups found that the strongest companies tend to be AI-native, global from launch, B2B-focused, and effective at retaining customers. Multifounder startups generally pull ahead over time, though exceptional bootstrapped solo founders can nearly match them.
## AI-Native Products
- Top-decile solo founders were about twice as likely to build products whose core functionality depends on AI models.
- By year two, AI-native startups generated nearly twice the revenue of other solo-founded companies.
- Their advantage was broad-based, spanning approximately the 50th through 95th revenue percentiles—not merely the result of a few extreme outliers.
- AI lowers the technical barrier, allowing founders to focus on solving problems quickly, shipping products, and finding distribution.
## Global Sales from Launch
- Top-performing solo founders sold to an average of 10 countries in their first month, compared with three for median founders.
- By month 24, they reached about 40 non-US countries, versus six for median founders.
- International customers generated 51% of top-decile revenue, compared with only 2% for median companies.
- Early access to large markets such as the US helped accelerate growth.
## B2B Business Models
- Top solo founders were nearly 30% more likely to build B2B companies.
- By month 24, the median solo B2B startup generated more than four times the revenue of the median B2C startup.
- Among top performers, B2B companies earned nearly twice as much as comparable B2C companies.
- This advantage persisted among bootstrapped startups, suggesting it was not primarily caused by easier access to funding.
## Early Customer Retention
- Nearly 30% of customers at top-decile startups returned the following month, compared with 8% at middle-decile companies.
- Top performers began recovering churned customers around three months earlier.
- By the start of year two, their first-month customers were spending 47% more than at acquisition—roughly twice the increase seen among middle-decile startups.
- In B2B, top solo founders retained initial customers at six times the rate of median founders.
- Recurring billing was more common among top performers, by 26 percentage points in B2B and 20 points in B2C.
## Solo Founders Compared with Teams
- Solo startups initially generated more revenue than multifounder startups, but multifounder companies led by month 24.
- Top-decile multifounder startups produced 53% more revenue than top-decile solo startups, even after accounting for funding.
- Among the very best bootstrapped companies, the gap narrowed to just 5%.
- Exceptional solo founders compensate for limited headcount through speed, resourcefulness, hiring, advisors, and founder networks.
Solo founders appear most likely to succeed when they use AI to move quickly, target business customers, sell internationally from the beginning, and validate demand through strong retention. Teams still offer a long-term advantage, but highly capable, well-connected solo founders can approach team-level performance without outside funding.
AWS has named four new Heroes for May 2026, recognizing leaders who advance cloud, AI, serverless, and community education. Their work ranges from building Amazon Bedrock-powered tools and contributing to AWS certifications to organizing major user groups and events across Europe and Latin America. Together, they demonstrate how technical expertise and community leadership can help more builders adopt AWS.
## Damiano Giorgi — Pavia, Italy
- An Artificial Intelligence Hero and Cloud Solutions Architect specializing in AI.
- Helps organize AWS User Groups in Pavia and Milan.
- Created the “Unofficial post:Invent Session Suggester,” using Amazon Bedrock and Amazon Nova to recommend re:Invent sessions.
- Shares knowledge through his “Bass and Bytes” blog and conferences across Europe.
## Darryl Ruggles — Ottawa, Canada
- A Serverless Hero and Cloud Solutions Architect with a background in software development.
- Focuses on AWS application architecture, AI/ML, serverless, containers, and FinOps.
- Publishes blog posts, LinkedIn content, and open projects.
- Participates actively in online communities such as “Believe In Serverless” and in-person AWS events.
## Ricardo Daniel Ceci — Buenos Aires, Argentina
- An Artificial Intelligence Hero leading the AWS User Group Buenos Aires, with nearly 2,400 members.
- Principal organizer of AWS Community Day Argentina.
- Named AWS Community Leader of the Year 2025 for Latin America.
- Hosts a podcast with cloud experts, AWS Heroes, and developer advocates.
- Works to make cloud and AI more accessible to Spanish-speaking builders across LATAM.
## Matias Kreder — Buenos Aires, Argentina
- An Artificial Intelligence Hero and AWS Certification Subject Matter Expert.
- Contributed to AI/ML certifications, including the AWS Certified AI Practitioner exam.
- Began his community involvement through AWS DeepRacer, qualifying as a finalist three times.
- Organizes racing events, ML talks, and AWS community activities across Latin America.
- Helped organize AWS Community Day Argentina 2025 and speaks at regional events.
These new Heroes illustrate the value of combining AWS expertise with mentorship, content creation, certification work, and community organizing. Builders can learn more or connect with regional leaders through the AWS Heroes program.
Issue 16 of Figma’s newsletter, “Trust the Process,” explores how AI and agentic tools are changing product design. Its central argument is that faster creation makes judgment, context, and craft more important—not less. Teams need to choose the right problems, preserve design intent, and build workflows that connect design and code.
## Choosing What’s Worth Shipping
- AI enables product teams to build and iterate rapidly.
- The main risk is moving quickly in the wrong direction or settling for “good enough” output.
- Strong product judgment and a clear sense of what creates meaningful differentiation remain essential.
## Using MCP to Preserve Context
- Model Context Protocol (MCP) allows coding agents to access context from Figma files and design systems.
- Figma’s MCP server helps developers translate design decisions into code more accurately.
- Better documentation and structured design systems can make this workflow more effective.
## Building Visual Workflows with AI
- Figma Weave supports AI-assisted work across video, photography, illustration, and 3D effects.
- The newsletter highlights more than 20 workflow templates and methods for creating asset libraries from reference images.
- Effective prompting depends on understanding the logic behind a visual language, including how to build, edit, and direct imagery consistently.
## The Design-to-Code Loop
- Modern teams increasingly move back and forth between canvas and code.
- This “roundtripping” gives designers and developers faster feedback and deeper product context.
- Keeping real product states connected to the canvas can reduce drift between what is designed and what ultimately ships.
- The convergence of design and development creates more opportunities to improve both speed and craft.
## Practical Experiments
- A workflow lab demonstrates how Figma MCP can help teams refine a video export flow by bringing real product states into the design canvas.
- Figma also offers efficiency tips for users who rely heavily on Figma Make, including ways to manage credits and streamline workflows.
The newsletter recommends treating AI as an accelerator rather than a substitute for direction. The best results come from combining faster tools with deliberate judgment, strong context, and continuous collaboration between design and code.
AWS’s latest updates focus on expanding regional infrastructure, improving developer workflows, and making cloud and AI services more portable. The Istanbul Local Zone strengthens data residency and low-latency capabilities in Türkiye, while tools such as ExtendDB, OpenAI-compatible SageMaker APIs, and Kiro Web reduce migration and development friction. Together, these releases emphasize flexibility, operational resilience, and easier local testing.
## AWS Local Zone in Istanbul
- AWS opened a new Local Zone in Istanbul, Türkiye.
- It provides nearby compute, storage, and networking with single-digit millisecond latency.
- Organizations can keep and process data within Turkish borders to support residency and compliance requirements.
- The zone supports latency-sensitive workloads in sectors such as finance, government, telecommunications, and healthcare.
- Applications can combine Istanbul infrastructure with the broader AWS Region, enabling hybrid architectures without operating a private data center.
## Security and AI Service Updates
- **Security Hub Extended** now integrates with 21 curated partner solutions across nine security categories, including endpoint protection, threat intelligence, and cloud security posture management.
- **Amazon SageMaker AI** supports OpenAI-compatible inference APIs, allowing existing OpenAI-based applications to use SageMaker with minimal or no SDK changes.
- **Secrets Manager Agent** can pre-fetch secrets at startup, reducing cold-start delays, and can assume IAM roles for workloads with different permission boundaries.
- **Amazon Bedrock** introduced tools for advanced prompt optimization and migration across foundation models.
## Open-Source and Local Development Tools
- AWS open-sourced **ExtendDB**, a DynamoDB-compatible adapter for alternative storage backends.
- It supports local development and testing without a live AWS connection.
- It can help teams retain DynamoDB API semantics while controlling the underlying storage layer.
- **AWS SAM CLI** now supports CloudFormation Language Extensions locally, improving consistency between local testing and production deployments.
## Developer Experience and Reliability
- **Kiro Web** brings AWS’s AI-assisted, spec-driven development environment to browsers, providing access to chat and agent capabilities without installing the desktop IDE.
- AWS updated default retry behavior across SDKs and CLI tools.
- Improvements include smarter backoff and better throttling handling.
- Production applications should become more resilient to transient failures without additional configuration.
## Container Image Changes
- Bitnami images are being removed from Amazon ECR Public.
- Teams currently using those images should review the migration timeline and update image references to Bitnami’s own registry to avoid interruptions.
## Upcoming AWS Events
- AWS Summit Amsterdam: May 27
- AWS Summit Bangkok: May 28
- AWS Summit Milan: May 28
Builders should evaluate the Istanbul Local Zone for residency- or latency-sensitive systems, consider ExtendDB and SAM improvements for local workflows, and review the Bitnami registry change before images are removed from ECR Public.
AI has made building products faster and more accessible, but speed alone no longer creates an advantage. When anyone can ship, the differentiators become choosing the right direction and shaping the result with care. Figma’s Yuhki Yamashita argues that strong teams combine rapid exploration, deliberate decision-making, and relentless craft.
## Choosing What’s Worth Building
- The abundance of possible ideas makes it easy to commit prematurely to the first promising concept.
- Iterating deeply on one idea can become “local hill-climbing,” refining a path without questioning whether it is the right one.
- AI tools can worsen tunnel vision by accelerating a chosen direction without challenging its assumptions.
- Traditional strategic methods, such as MECE option mapping, encourage breadth but can remain too abstract to create conviction.
- A stronger approach combines breadth and depth:
- Explore several distinct directions in parallel.
- Turn each direction into a realistic, end-to-end interactive prototype.
- Compare actual user experiences rather than abstract diagrams or wireframes.
- Invite teammates and AI agents to react and build on ideas collectively.
- This creates a more collaborative, parallel way of working instead of a siloed, sequential process.
## Making the Product Yours
- AI-generated products tend to converge on familiar patterns and statistically likely solutions.
- “Good enough” becomes easy to produce and easy to accept, creating interchangeable products.
- The main danger is passivity: accepting the first convincing result because it looks polished.
- Craft requires active judgment:
- Question every decision.
- Revisit and refine multiple times.
- Remove unnecessary elements.
- Push beyond the first few acceptable versions.
- Develop a distinct point of view.
- As AI raises the baseline quality of products, differentiation will come less from tools or execution speed and more from the care and intention behind the final result.
## What Matters Now
- The essential capabilities are **speed, direction, and craft**.
- The best teams do not treat these as competing priorities:
- They move quickly.
- They choose deliberately.
- They refine relentlessly.
- In a world where nearly anything can be built, the lasting advantage lies in deciding what deserves to exist and shaping it into something distinctive.
Figma introduces a design agent built directly into its canvas and left rail. Unlike external tools, it understands a team’s components, tokens, libraries, standards, and best practices, while preserving designers’ ability to manipulate files directly. The agent is intended to support exploration, iteration, collaboration, and repetitive production work without forcing a choice between AI speed and design precision.
## A Figma-native design agent
- Works inside the same Figma file as the team, acting as a collaborative partner.
- Can start from any design layer and generate or edit Figma layers.
- Supports parallel prompting to explore multiple ideas simultaneously.
- Lets designers continue making manual edits while the agent works.
- Uses context from frequently and recently used components, with additional control through selected libraries and `@` mentions for tokens, variables, and components.
- Is designed for direct manipulation and editing of Figma files, rather than simply producing external suggestions.
## How the agent works with MCP and Figma Make
- The Figma agent is intended for canvas-based work and has deeper design-system context.
- Figma’s MCP server and `use_figma` support movement between code and the canvas:
- Pull code into Figma for iteration or design-system application.
- Push designs back to code while maintaining fidelity.
- Teams can begin in Figma Design, use the agent to clarify flows, states, copy, and structure, then send work to Figma Make to generate code layers.
- Alternatively, teams can start in Figma Make, copy frames into Figma Design, refine them with the agent, and return them to Make.
## Exploring more design directions
- The agent helps designers generate several approaches instead of settling for the first plausible result.
- It can:
- Produce distinct stylistic directions for the same design.
- Compare checkout flows optimized for different business goals.
- Generate alternative information architectures.
- Create multiple screen or layout variations.
- Example prompts include generating organic, modern, and retro style options, or producing image carousels with different title treatments.
- Once a direction is selected, hands-on editing remains an efficient way to refine the design and reduce unnecessary prompting.
## Automating repetitive design work
- The agent handles bulk operations that require both scale and design context.
- Potential tasks include:
- Renaming variables consistently.
- Replacing components across many screens.
- Applying padding changes throughout a flow.
- Populating frames with realistic content.
- Updating typography across a file.
- Replacing placeholder text and imagery.
- Setting chip components to active states.
- Converting screens to dark mode with appropriate fill and contrast changes.
- For design-system teams, it can help update library descriptions, tags, use cases, naming conventions, and component documentation.
- This automation is designed to preserve momentum between AI-generated changes and precise manual adjustments.
The practical recommendation is to use the Figma agent for broad exploration and context-heavy repetitive work, while retaining direct canvas manipulation for judgment, refinement, and final design decisions.
Empirical Research Assistance (ERA) is a Google AI system designed to help scientists develop expert-level computational models. Using Gemini, it searches literature, generates and evaluates code, and explores thousands of possible solutions through tree search. A Nature paper reports strong performance across scientific benchmarks, while new applications suggest ERA can accelerate research in health, climate, energy, and economics.
## How ERA Supports Scientific Coding
- ERA starts with a scientific problem and a success metric.
- It searches relevant research, combines methods, writes code, and iteratively tests and improves solutions.
- Its tree-search process evaluates thousands of alternatives to optimize the resulting model.
- Benchmarks in genomics, public health, satellite imagery, neuroscience, time-series forecasting, and mathematics showed expert-level performance.
## Applications to Open Scientific Problems
- **Epidemiological forecasting**
- Predicted U.S. hospital admissions up to four weeks ahead for flu, COVID-19, and RSV.
- Forecasts ranked at or near the top of CDC leaderboards.
- The techniques can potentially be adapted to other countries and diseases.
- **California water-supply forecasting**
- Produced seasonal runoff predictions for snow-fed river basins.
- Delivered more accurate early forecasts than California’s official Bulletin 120 outlook.
- Improved predictions could support water management and agriculture.
- **Atmospheric carbon dioxide monitoring**
- Combined geostationary weather-satellite data with other inputs to estimate CO₂ concentrations every 10 minutes across broad areas.
- Captured urban emissions, plant-driven daytime absorption, and other atmospheric cycles.
- Provides higher spatial and temporal coverage than measurements from satellites such as Orbiting Carbon Observatory-2.
- **Solar-energy design**
- Combined ERA with Google Antigravity to optimize three-dimensional solar-panel geometries.
- Identified a 500-triangle volumetric fan design that could capture scattered radiation without backward shading.
- **Retail forecasting**
- Used economic indicators, Google Trends, historical patterns, and consumer sentiment.
- Matched or exceeded commercial consensus forecasts and the Chicago Fed’s monthly retail forecast.
## Computational Discovery
- Google is gradually opening access to Computational Discovery through a trusted tester program in Google Labs.
- The system combines ERA with AlphaEvolve to support computational scientific investigation.
- It complements other Gemini for Science experiments:
- **Hypothesis Generation**, built with AI Co-Scientist, supports developing scientific hypotheses.
- **Literature Insights** supports research and literature analysis.
ERA’s demonstrated value lies in automating the labor-intensive cycle of designing, testing, and refining scientific software. Its expanding applications indicate that AI-assisted computational research could broaden access to advanced modeling while helping experts investigate complex scientific problems more quickly.
TPM roles are often associated with coordinating schedules, dependencies, risks, and stakeholders. Toss argues that this is no longer enough: as organizations grow and AI increases cross-team complexity, the most important problems often fall into gray areas with no clear owner. Its TPM is therefore redefined as a strategic execution problem-solver who structures ambiguous problems and drives them to measurable resolution.
## Why TPM Needs to Be Redefined
- Traditional TPMs typically deliver already-defined technical programs by managing:
- Schedules
- Risks
- Dependencies
- Cross-functional communication
- At Toss, many difficult problems do not begin as clearly named programs.
- Common examples include:
- Problems spanning multiple teams with no accountable owner
- Strategies without an execution model
- Issues recognized as important but lacking priority or authority
- Frequent status updates without meaningful change
- These problems may involve product, technology strategy, organization design, and operations simultaneously.
- AI adoption is accelerating this trend by increasing dependencies across data, security, quality, productivity, and organizational practices.
## How Toss’s TPM Differs from Related Roles
- **Product Owner:** Defines what to build, product priorities, and customer or business value.
- **Engineering Manager or SDM:** Builds the conditions for a team to execute consistently, including people, quality, and team health.
- **Traditional TPM or Technical Project Manager:** Manages delivery of an already-defined initiative.
- **Toss TPM:** Addresses the structural problems left between or outside these roles.
- Finds important but undefined problems
- Establishes ownership and decision rights
- Creates an executable structure
- Drives the work through to completion
- The role is not primarily a project scheduler or people manager; it is a problem solver for organizational gray areas.
## Why Cross-Team Problems Matter in Strong Organizations
- In less mature organizations, bottlenecks such as unclear responsibility or poor prioritization are usually visible within teams.
- In high-performing organizations, individual teams may operate effectively while problems remain between teams.
- Organizational structures clarify accountability and speed decisions, but they can also leave boundary-spanning issues without an owner.
- These issues include:
- Company-wide problems that local optimization cannot solve
- Important long-term work that is not urgent
- Responsibilities shared by several teams but owned by none
- AI makes these boundary problems more frequent because technical, operational, and organizational concerns increasingly overlap.
## What a Toss TPM Does
- **Finds problems proactively**
- Identifies recurring gaps, structural bottlenecks, and unnamed problems rather than waiting for assigned work.
- **Turns strategy into execution**
- Determines which teams must act, in what order, who should be the DRI, and what must be deprioritized.
- **Creates value between teams**
- Designs solutions where different goals, constraints, and working speeds collide.
- **Removes blockers**
- Goes beyond reporting risks by changing decision structures, assembling the right people, resetting priorities, or redesigning collaboration.
- **Considers people and systems together**
- Examines leadership, team composition, authority, and operating mechanisms—not just timelines.
- **Measures success through real change**
- Success means execution resumes, direction improves, recurring bottlenecks decrease, and future solutions become easier.
- Coordination is a useful skill, but problem-solving is the role’s core identity.
## Capabilities Needed to Become This Kind of TPM
- **Problem structuring:** Separating symptoms from root problems, identifying stakeholders, and locating decision bottlenecks.
- **Execution design:** Translating strategic direction into concrete workflows, sequencing, and ownership.
- **Influence and mobilization:** Moving teams without relying solely on formal authority, including handling difficult conversations.
- **Systems thinking:** Addressing repeated problems by changing mechanisms rather than relying on individual heroics.
- **Follow-through:** Carrying work from discovery and alignment through execution, measurable results, and prevention of recurrence.
Toss’s recommendation is to look for important problems that everyone recognizes but no one owns. People who cannot ignore those gaps can begin acting as informal TPMs in their current organizations—turning ambiguous, cross-functional problems into executable solutions and driving them to completion.
Vertical SaaS platforms are responding to AI pressure by becoming more deeply embedded in customers’ operations rather than relying on software features alone. Payments, lending, compliance, and other financial or operational services create stronger retention and revenue opportunities, while AI products help platforms remain competitive at the software layer. The post concludes that platforms should monetize AI experimentally and prepare to support emerging agentic commerce.
## Expanding Beyond Software
- AI makes software features easier to replicate, but vertical platforms retain an advantage through deep industry knowledge and workflow integration.
- Embedded payments connect platforms to transaction processing, revenue tracking, and cash-flow management.
- Median payments adoption increased from 27% in 2024 to 40% in 2025, while top Stripe platforms exceed 80%.
- Successful companies make payments a company-wide priority:
- Include payments in sales demos and compensation plans.
- Set goals beyond Gross Payment Volume, including company-wide ARR.
- Reinforce adoption through onboarding and customer success.
- Embedded payments can generate approximately $4,200 in incremental ARR per adopting customer.
- Platforms offering embedded financial products experience 11% lower annual churn, while multiproduct platforms grow revenue 49% faster than software-only peers.
## Building Operational and Financial Moats
- Payments can lead to additional services such as capital, banking, cards, payroll, and bill payment.
- TheCut’s Stripe Capital program generated $788,000 in accepted financing from 167 barbers within 24 hours.
- Financial products help businesses purchase equipment, manage seasonal slowdowns, and fund marketing.
- Operational services can also create defensibility:
- Moxie embeds compliance tools to help medspas maintain licenses.
- Slice negotiates wholesale pizza-box pricing for restaurants.
- These specialized services are difficult for a new AI-native competitor to reproduce immediately.
## Developing Vertical AI Products
- Most surveyed SaaS platforms—87%—see AI more as an opportunity than a threat.
- Platforms are adding industry-specific AI tools, including:
- Toast IQ, which identifies local food trends for restaurants.
- Quipli, which generates leads from newly filed equipment-rental permits.
- Clio’s assistant, which drafts legal documents, summarizes files, and surfaces client insights.
- AI is positioned as a way to automate repetitive work while using the platform’s existing customer and industry context.
## Experimenting with AI Pricing
- Eighty-six percent of SaaS platforms with AI features charge for them.
- Pricing models include:
- Bundling AI into existing subscriptions.
- Premium tiers.
- Stand-alone usage-based or outcome-based pricing.
- Since 44% of platforms expect to change their AI pricing within a year, companies should test willingness to pay before committing to a model.
- Charging separately can help determine whether AI delivers meaningful customer value.
## Preparing for Agentic Commerce
- AI agents are expected to influence product discovery, purchasing decisions, and checkout.
- Platforms are preparing with agent-readable catalogs and headless checkout APIs.
- This infrastructure is intended to support a projected $5 trillion agentic-commerce opportunity.
- Retail platforms still face foundational challenges, particularly inconsistent or poorly structured product data optimized for human shoppers.
Vertical SaaS companies should combine AI innovation with deeper operational integration. The strongest long-term strategy is to offer industry-specific automation while using payments, financial services, and specialized workflows to become indispensable to customers.
Cloudflare announced plans to eliminate more than 1,100 jobs as it restructures for an “agentic AI era.” The company says the decision reflects a fundamental redesign of internal processes, teams, and roles—not individual performance or a conventional cost-cutting exercise. Leaders argue that acting decisively now will reduce prolonged uncertainty and create a faster, more innovative organization.
## Restructuring Around Agentic AI
- Cloudflare’s internal AI usage has increased by more than 600% in three months.
- Employees across engineering, HR, finance, marketing, and other departments run thousands of AI-agent sessions daily.
- Because Cloudflare uses AI extensively itself, leadership says the company must redesign how work is organized to capture its benefits.
- The restructuring covers internal processes, teams, and roles across the company.
## A Company-Wide Workforce Reduction
- More than 1,100 employees globally will leave Cloudflare.
- The company emphasizes that departures are not judgments about employees’ talent or performance.
- Founders Matthew Prince and Michelle Zatlyn are communicating the decision directly rather than routing notices through managers.
- Every employee is receiving an email explaining how the changes affect them.
## Severance and Treatment of Departing Employees
- Departing employees will receive the equivalent of their full base salary through the end of 2026.
- U.S. healthcare support will continue through the end of 2026.
- Equity will continue vesting through August 15.
- Employees who had not reached their one-year vesting cliffs will receive prorated vesting through August.
- Cloudflare frames these benefits as an effort to treat departing employees with empathy and exceed typical industry standards.
## Why Cloudflare Chose Decisive Action
- Leadership argues that smaller, repeated layoffs or a prolonged reorganization would create continuing emotional uncertainty.
- Completing the changes at once is intended to provide clarity to departing employees and stability for those who remain.
- Cloudflare believes its original cloud-native structure helped it surpass older companies with slower systems and processes.
- As the company has grown, it says it must avoid relying on organizational structures that worked in the past.
## Looking Ahead
- Cloudflare expects the reshaped organization to operate more quickly and innovate more effectively.
- The founders planned to discuss the announcement during the company’s earnings call and an all-hands meeting.
- They presented the restructuring as necessary to continue advancing Cloudflare’s mission of building a better Internet.
The practical conclusion is that Cloudflare is making a large, one-time organizational reset to align its workforce with AI-driven operations, while offering unusually extensive severance intended to reduce the disruption for affected employees.
Grammarly’s inaugural Educator of the Year Award honors teachers nominated directly by their students. The first winner, Dr. Humberto López Castillo of the University of Central Florida, is recognized for teaching precise, accessible communication and applying it to public health, technology, and community engagement. His approach combines audience-aware writing, responsible AI use, and hands-on research.
## Student-Led Recognition
- Students nominate educators through short videos describing their impact on academic and professional development.
- UCF student Vardhan Avaradi nominated Dr. López Castillo for encouraging students to make their language “precise yet accessible.”
- López Castillo is a pediatrician, public health researcher, translator, and four-language polyglot from Panama.
- His teaching emphasizes collaboration and the connection between individual health and broader communities.
## Communicating With Different Audiences
- Students translate complex public health topics for audiences outside academia.
- Assignments have included:
- Storybooks about mosquitoes for kindergarteners
- Monopoly-style games about living with HIV
- Rap songs explaining tuberculosis
- Podcasts that personalize epidemiology
- His medical experience informs this approach: communication must change depending on whether the audience is a child, parent, or professional researcher.
## AI Requires Human Judgment
- López Castillo permits students to use AI for drafting but expects them to verify and critically evaluate its output.
- When AI-generated citations referenced nonexistent research, he treated the error as a lesson rather than a punishment.
- He compares AI to a calculator: useful and powerful, but dependent on the judgment of the person using it.
- He and Vardhan are developing a machine learning project using the NIH All of Us dataset, which contains nearly one million de-identified health records.
- Their research explores using AI to classify populations and predict health risks.
## Preparing Students for Broader Impact
- Students leave with stronger writing, critical-thinking, collaboration, and communication skills.
- López Castillo’s teaching focuses not just on adopting new tools, but on using them responsibly and communicating with purpose.
- His students learn to reach people beyond academic audiences while keeping human needs at the center of technology and research.
The post’s central recommendation is to pair emerging technologies with critical thinking, audience awareness, and a strong sense of social responsibility.
Empirical Research Assistance (ERA) is being used by Google researchers to tackle practical scientific problems rather than only benchmark exercises. Early applications span public-health forecasting, cosmology, and climate monitoring, showing that AI can improve prediction, solve difficult mathematical problems, and extract new value from existing data. The results suggest ERA could make advanced computational research more accessible while producing interpretable, scientifically grounded models.
## Public Health Forecasting
- Google expanded ERA-based hospitalization forecasts from COVID-19 to influenza and RSV.
- The team submits weekly forecasts for every U.S. state, covering horizons of up to four weeks.
- Google forecasts have performed at or near the top of public CDC flu and COVID-19 leaderboards, with similarly strong internal results for RSV.
- Forecast accuracy is evaluated using the Weighted Interval Score on log-transformed hospitalization data.
- This approach could broaden access to epidemiological modeling and support forecasting for more diseases and regions.
## Cosmology: Cosmic Strings
- Cosmic strings are theoretical spacetime defects that may emit gravitational radiation.
- Calculating their radiation spectrum is difficult because the governing equations contain singularities.
- Earlier work found only a partial solution for a square loop with an angle of 90 degrees.
- By combining ERA with Gemini Deep Think, researchers derived six general solutions and a concise formula for the asymptotic limit.
- The result demonstrates how AI systems can help explore advanced mathematical techniques and address previously unsolved cosmological problems.
## Climate Monitoring with Weather Satellites
- Existing CO₂ satellites provide highly precise but infrequent and geographically limited measurements.
- Geostationary satellites such as GOES East scan large areas every 10 minutes, but were not designed to measure CO₂.
- Researchers used ERA to create a physics-guided neural network that combines:
- 16 GOES East wavelength bands
- Lower-troposphere meteorology
- Solar angles
- Time of year
- Trained using sparse OCO-2 and OCO-3 observations, the model estimated column-averaged CO₂ continuously across the satellite’s coverage area.
- Comparisons with independent satellite and ground-based observations showed that it captured real CO₂ variation.
- The work illustrates how AI can repurpose existing instruments and improve the value of expensive scientific datasets.
ERA’s early applications indicate that AI-assisted empirical software can support accurate forecasting, novel mathematical discovery, and higher-resolution environmental monitoring. Its greatest potential may lie in combining domain expertise with existing data and infrastructure to solve problems that would otherwise require substantial time and specialized resources.
Gemini Enterprise is designed to make complex, multi-agent business workflows feel simple without hiding AI’s role. Its core principle is to keep users focused on goals while making intervention, accountability, and data context visible. The result is an agentic system that supports not only individual productivity but shared team intelligence.
## A Familiar Brand with Business-Specific Capabilities
- Gemini Enterprise shares Gemini’s visual language, including the sparkle icon, gradients, rounded shapes, and motion.
- Its enterprise experience emphasizes integrations with tools such as Google Workspace, Jira, and Notion.
- Connectors are made prominent in the prompt experience so agents can access the business context needed to produce useful results.
## Moving Beyond Chat with the AI Inbox
- Enterprise work often involves multiple tools, data sources, deadlines, and agents working simultaneously.
- The AI Inbox provides a visual overview of:
- Tasks agents are currently handling
- Completed work
- Items requiring human intervention
- Deliverables awaiting review
- This dashboard is intended to feel more like a team status check-in than a sequence of chat messages.
## Collaborative Projects as Shared Workspaces
- Gemini Enterprise replaces isolated chat threads with persistent, shared project spaces.
- AI participates as a visible team member by:
- Performing tasks
- Summarizing discussions
- Finding project files
- Answering questions about shared material
- Requests are attributed to individual team members, improving accountability and helping others understand the context behind an agent’s actions.
- Shared spaces reduce information silos by allowing teammates to discover and use one another’s uploaded materials.
- The assistant becomes a single source of truth and a “team intelligence amplifier,” rather than merely a personal productivity tool.
## Multiple Modes of Team Interaction
- Teams can communicate with AI in group chats within Collaborative Projects.
- In Canvas Mode, the assistant can generate and edit documents.
- These modes allow AI to remain embedded in ongoing team workflows instead of being limited to isolated prompts.
Gemini Enterprise’s design recommendation is to combine powerful orchestration with clear visibility and human control. Agents should work proactively, but their actions, sources, status, and opportunities for intervention must remain understandable to the people responsible for the outcome.
LY Corporation’s Orchestration Development Workshop promotes a shift from using AI as a code suggestion tool to using it as an autonomous development “pilot.” The team combined specification-driven development with Jira/Confluence access through MCP so AI agents could investigate requirements, plan implementations, write code, run checks, create pull requests, and respond to reviews. The approach improved development speed and planning, but still requires humans to review AI-generated code and retain responsibility for product quality.
## From Copilot to Agentic Coding
- The team initially used GitHub Copilot for small code-generation tasks but saw limited productivity gains.
- Two developments enabled a broader shift:
- Existing requirements and design documents supported specification-driven development.
- Jira and Confluence became accessible to AI coding tools through the Model Context Protocol (MCP).
- Agentic coding gives an AI agent a high-level goal, which it decomposes into tasks and executes iteratively.
- Unlike autocomplete tools, an agent can analyze the broader codebase, run commands and tests, fix lint errors, and continue until the requested feature is complete.
## Workshop Goals and Human–AI Responsibilities
- The workshop focused on automating the process from implementation through pull-request creation.
- Humans remained responsible for:
- Defining requirements and writing specifications
- Final testing, review, and release decisions
- AI was assigned:
- Implementation planning
- Code implementation
- Pull-request creation
- Initial review and review-response work
- This division preserved the existing development process while giving participants practical experience with agentic workflows.
## Stage 1: Research and Implementation Planning
- Participants supplied a Jira ticket URL to a custom slash command.
- The AI agent:
- Retrieved Jira data through the Jira MCP tool
- Followed Epic links and collected related tickets
- Retrieved Confluence documentation through the Confluence MCP tool
- Explored the codebase, using an Explore Agent where available
- Wrote a detailed implementation plan to `specs/{ticket-or-topic}/plan.md`
- The plan included requirements, affected components, technical analysis, implementation tasks, risks, testing considerations, and a checklist.
- Saving the plan to a file made it available for human review, future sessions, and later PR generation.
- The team emphasized planning early because vague instructions can lead to incorrect implementations and costly rework.
## Stage 2: Implementation and Pull-Request Creation
- The reviewed plan was passed to an implementation command.
- The AI was instructed to:
- Understand the plan and implementation scope
- Modify the code
- Add or update tests
- Run the test suite
- Run linting and build commands
- Fix any resulting problems
- Explicitly listing these steps encouraged the coding agent to maintain a task checklist and complete the full development cycle.
- A separate PR command generated the pull request using the team’s template.
- Information gathered during the planning stage could be reused in the PR description, reducing administrative work.
## Stage 3: AI Review and Issue Resolution
- An AI screening-review command analyzed the generated PR.
- It also read existing comments, including:
- The AI’s own prior review comments
- Comments from other team members
- The agent identified issues requiring changes and explained its assessment of existing comments.
- After a human reviewed those conclusions, the AI could implement the necessary fixes, reducing the cost of responding to review feedback.
## Benefits and Risks
- **Higher code-generation speed**
- Agents can work with less frequent human intervention.
- Developers can perform other tasks while agents work.
- Multiple agents can potentially run in parallel.
- **Earlier risk discovery**
- Detailed implementation plans clarify the work before coding begins.
- Planning can reveal overlooked tasks, dependencies, and risks.
- **Greater review burden**
- AI can generate large volumes of code that humans must still inspect.
- The unfamiliar workflow may create stress for developers.
- **Human accountability remains essential**
- Developers are responsible for the quality of AI-generated code.
- Poor-quality output increases reviewer workload and can add technical debt.
## Workshop Results
- The workshop was delivered twice:
- A hands-on practical session requiring prior preparation
- An introductory session with more detailed support
- Approximately 2,500 people participated.
- More than 40% of respondents had already applied, or intended to apply, some aspect of the workshop.
- The sessions provided concrete guidance on MCP server usage and effective ways to delegate coding tasks to AI agents.
The recommended approach is to introduce agentic coding incrementally: keep human ownership of requirements and final quality decisions, while allowing AI to handle structured planning, implementation, testing, PR creation, and initial review.
