Attendee is the open source API for building meeting bots on Zoom, Google Meet, and Teams. Ship meeting transcripts and recordings in days, not months.

Vance, an inveterate promoter of racist conspiracy theories, alleged at a press conference for the Fraud Task Force that Microsoft conspires to place job ads in obscure small-town papers. Then, according to Vance, Americans fail to see the ads, and Microsoft can claim it advertised the job to citizens before abusing H-1B holders as a pool of forced, foreign labor.

A Microsoft spokesperson directed Gizmodo to a blog post stating company representatives “look forward to providing the Administration with additional information.” Of the roughly 6,000 H-1B applications Microsoft submitted during the last fiscal year, the statement read, “80% were to extend or change the status of existing Microsoft employees.”

“The remaining filings for new employees were for individuals already legally in the United States who decided to come work for us, and they equal only 1% of our U.S. workforce. They are not new arrivals to our country,” Microsoft added in the post. The company wrote that it pays H-1B employees the same rates as anyone else doing comparable work.

Vance’s announcement came hours before Trump personally presented Microsoft CEO Satya Nadella, a former H-1B visa holder, with the National Medal of Technology and Innovation. Also honored: Tesla/SpaceX CEO Elon Musk, who has said he used to have an H-1B visa, as well as Google co-founder Sergey Brin, Nvidia CEO Jensen Huang, and Advanced Micro Devices CEO Lisa Su, all of whom are immigrants.

1. PMF Metric

How would you feel if you could no longer use Simulator by GoPractice!? 2. Recommendation

Have you recommended Simulator by GoPractice! to anyone? 3. Referrer

How did you discover Simulator by GoPractice!? 4. Alternatives

What would you likely use as an alternative if Simulator by GoPractice! were no longer available? 5. Benefits What is the primary benefit that you have received from Simulator by GoPractice!?

NB: please note that these are 5 random query results. You can see the rest of the results further below. A lot of practice a lot of practice Engaging way to get a data-driven mindset Working with analytics tool Working with Amplitude 6. Audience What type of person do you think would benefit most from Simulator by GoPractice!?

NB: please note that these are 5 random query results. You can see the rest of the results further below. A beginner PM a beginner PM Anyone in analytics department or product dep. Junior and middle product managers Anyone in the field 7. Improvement How can we improve Simulator by GoPractice! to better meet your needs?

NB: please note that these are 5 random query results. You can see the rest of the results further below. No idea no idea Make it shorter Use more gamification

The product-market fit definitions I had found were vivid and compelling, but they were lagging indicators — by the time investment bankers are staking out your house, you already have product-market fit. Instead, Ellis had found a leading indicator: just ask users “how would you feel if you could no longer use the product?” and measure the percent who answer “very disappointed.”

After benchmarking nearly a hundred startups with his customer development survey, Ellis found that the magic number was 40%. Companies that struggled to find growth almost always had less than 40% of users respond “very disappointed,” whereas companies with strong traction almost always exceeded that threshold.

A helpful example comes from Hiten Shah, who posed Ellis’ question to 731 Slack users in a 2015 open research project. 51% of these users responded that they would be very disappointed without Slack, revealing that the product had indeed reached product-market fit when it had around half a million paying users. Today, this isn’t too surprising, given Slack’s legendary success story. Truly, this example shows just how hard it is to beat the 40% benchmark.

With only 22% opting for the “very disappointed” answer, it was clear that Superhuman had not reached product-market fit. And while this result may seem disheartening, I was instead energized. I had a tool to explain our situation to the team and — most excitingly — a plan to boost our product-market fit

As an early-stage team, you could just narrow the market with preconceived notions of who you think the product is for, but that won’t teach you anything new. If you instead use the "very disappointed" group of survey respondents as a lens to narrow the market, the data can speak for itself — and you may even uncover different markets where your product resonates very strongly.

To start, we grouped the survey responses by their answer to the first question (“How would you feel if you could no longer use Superhuman?”):

We then assigned a persona to each person who filled out a survey.

Next, we looked at the personas that appeared in the very disappointed group — the 22% that were our biggest supporters — and used those to narrow the market. In this simplified example, you can see we focused on founders, managers, executives and business development  — temporarily ignoring all other personas.

With this more segmented view of our data, the numbers shifted. By segmenting down to the very disappointed group that loved our product most, our product-market fit score jumped by 10%. We weren’t quite at that coveted 40% yet, but we were a lot closer with minimal effort.

Policy as Code,Simplified!

Secure, automate, and operate all your infrastructure and applications with YAML and CEL based policies.

Oxylabs Residential Proxies Review: Strong Enterprise Pool, Expensive Below 1TB

Use Aircall wherever your
your team works

One platform for teams and AI agents to manage customer conversations. At the office, on the road, or on the go.

one open-source platform for uptime, incidents, on-call, status pages, logs, traces, metrics & APM.

When things go wrong, be the first to know — and the fastest to fix.

OneUptime replaces a whole shelf of SaaS tools with one platform you can self-host for free. It catches the outage, pages the right person, updates your status page, finds the root cause, and even opens the fix PR.

Does Jev learn a classification boundary from four examples?

• The Sam ​Altman-led company has been conducting a broad ​review of the activities of its AI models ⁠after the accidental hacking of Hugging Face.

• OpenAI is ​searching through roughly 50 petabytes of data as it ​works to understand the full scope of its rogue agent activity.

• A string of high-profile breaches globally by rogue AI agents ​in recent months has sparked widespread worries within ​the AI industry over its ability to control the more powerful ‌AI ⁠models now under development.

• "In some cases, models used internet access in unintended ways or, in retrospect, did not have the ideal restrictions applied. Over the last ​several months, ​we have ⁠been applying new technical and operational measures to avoid similar problems, or catch ​them very early, and will continue this ​work," ⁠OpenAI said.

• OpenAI previously said the review would take months to complete given the scale of the work.

• The Hugging Face incident ⁠remains ​the most severe rogue agent ​activity OpenAI has identified from its AI models so far.

What Is Jev? The AI Model That Doesn't Generate Text

TypeSafe AI is releasing our first System One Model: a new class of frontier models built to make fast, structured decisions that software can use directly.

We built a new stack entirely focused on automation: with a new model architecture, parallel sampler for maximum efficiency, and training method we call Reinforcement Learning for Calibrated Decisions (RLCD).

Our first public model is Jev, available today in early access. Jev achieves similar levels of intelligence on System One tasks compared to existing LLMs, while being two orders of magnitude faster and more efficient. While Jev gives up string generation, it’s optimized for structured outputs and can’t hallucinate. 

Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out. 

Extraordinary claims require extraordinary evidence so see below for the receipts. 💅

A Virtual Machine Monitor for modern Cloud workloads. Features include CPU, memory and device hotplug, support for running Windows and Linux guests, device offload with vhost-user and a minimal compact footprint. Written in Rust with a strong focus on security.

Firecracker is an open source virtualization technology purpose-built for creating and managing secure, multi-tenant container and function-based services. It enables you to deploy workloads in lightweight virtual machines (microVMs) that provide enhanced security and workload isolation over traditional VMs, while enabling the speed and resource efficiency of containers.

For teams that want the capabilities of Firecracker without managing infrastructure, Lambda MicroVMs is a fully managed serverless compute primitive. Built on the same Firecracker virtualization powering 15 trillion+ monthly Lambda function invocations, it provides isolated sandboxes with near-instant launch, state persistence for up to 8 hours, and automatic suspend/resume. No capacity planning required.

In a January 2025 ethics disclosure, Kennedy said he would earn a 10% contingency fee when he referred cases to a law firm that represented plaintiffs suing Merck over the HPV vaccine Gardasil.

Kennedy received over $850,000 through this deal in 2024, the disclosure said. However, Kennedy said last month he planned to divest his interest in prior litigation against Merck, and that any proceeds from the lawsuit would go to one of his sons.

This guide provides you with a list of the requirements needed before submitting your integration for publication in the Okta Integration Network (OIN) (opens new window).