Latest news with #DynamoAI
Gulf Insider
a day ago
- Business
- Gulf Insider
Indian Engineer Caught Secretly Working Up To Five Tech Jobs Simultaneously, Offered New Gig At "Cheat On Everything" Startup
A Silicon Valley hiring scandal has erupted around an Indian software engineer accused of secretly working for as many as five technology companies simultaneously, exposing vulnerabilities in startup recruitment practices and reigniting debates over remote work accountability. Soham Parekh, based in India, faces mounting allegations from multiple Y Combinator-backed startup founders who claim he misrepresented his employment history while juggling concurrent positions. Suhail Doshi, founder of artificial intelligence startup Playground AI, first exposed the alleged scheme in a series of posts detailing how his company discovered Parekh's simultaneous employment at three to four other firms. Playground AI terminated Parekh within a week of hiring him in 2024, Doshi said. PSA: there's a guy named Soham Parekh (in India) who works at 3-4 startups at the same time. He's been preying on YC companies and more. Beware.I fired this guy in his first week and told him to stop lying / scamming people. He hasn't stopped a year later. No more excuses. — Suhail (@Suhail) July 2, 2025 Probably 90% fake and most links are gone. — Suhail (@Suhail) July 2, 2025 'His resumé was 90% fake,' Doshi wrote, sharing documentation that listed previous positions at prominent AI companies including Dynamo AI, Union AI, and Synthesia. The founder also published what he claimed was a private message from Parekh expressing remorse: 'Have I completely sabotaged my career? What can I do to improve my situation? I am also happy to come clean.' The allegations quickly snowballed as other tech executives came forward with similar experiences. Fleet AI CEO Nicolai Ouporov claimed that Parekh had worked for his company and suggested the behavior was systematic. This Soham guy is devious! Proud so say we only worked with him for 1 week. I have friends who employed him for years. — Nicolai Ouporov (@nicolas_ouporov) July 2, 2025 Matthew Parkhurst, CEO of cloud cost optimization startup Antimetal, described firing Parekh after discovering his multiple concurrent positions. Despite calling the engineer 'really smart and likeable,' Parkhurst quipped that 'hiring Soham is a new rite of passage' in the startup community. Funnily enough, Soham was our first engineering hire in 2022Really smart and likable; enjoyed working with himWe realized pretty quickly that he was working at multiple companies and let him goI can't imagine the amount of equity he's left on the table — Matt (@mprkhrst) July 2, 2025 Yet, the controversy surrounding Parekh shouldn't take anyone by surprise. In 2024, research from found that one-third of remote workers maintain multiple jobs, compared to 20% of in-person workers and 17% of hybrid workers. 25% of employees hold multiple positions, averaging 50 hours per week across their roles. 'Remote workers are increasingly working multiple jobs due to the rising cost of living in recent years,' Kevin Thompson, finance expert and CEO of 9i Capital Group, told Newsweek. 'Additionally, many remote roles are relatively easy to manage, allowing workers to take on additional jobs without a noticeable drop in productivity.' The controversy has even attracted opportunistic job offers, with Cluley CEO Roy Lee, whose AI tool helps users 'cheat on everything' from job interviews to sales calls, reaching out to Parekh with a job offer. will report back soon — Roy (@im_roy_lee) July 3, 2025
Forbes
27-03-2025
- Forbes
AI-Driven Video Production: Architecting The Next Generation Of Tools
Konstantin Hohr is the Founder and CEO of Revideo, former Software Engineer at Jodel and DynamoAI. The video content creation landscape is changing. While traditional timeline-based editors have served the industry well, they're increasingly insufficient for modern content demands, particularly when dealing with programmatically generated assets and complex animations. This article examines the technical infrastructure enabling the next generation of video creation tools, drawing from my experience building Revideo's programmatic video framework. Video editing software traditionally works with timelines—strips of video, images and audio that can be arranged and modified. While this works for linear editing, it becomes limiting when trying to create dynamic content programmatically. My research has led me to a different approach: Instead of a timeline, code provides a more powerful way to describe how elements move and change over time. Making use of the HTML canvas as a universal render target, animations can be described through a TypeScript-based framework. This architecture offers several advantages. The canvas element provides a standardized, high-performance rendering API that's well-supported across platforms. By expressing animations in TypeScript, you gain type safety and better tooling support, while enabling developers to create reusable components and complex animation patterns that would be unwieldy in traditional timeline-based editors. Getting large language models (LLMs) to reliably generate code in a framework that isn't widely included in training data is surprisingly challenging. I have developed several strategies to make sure the output conforms to the desired syntax. First, context-enriched prompting is a must. Providing parts of the framework's documentation as a guide to outline the available API, as well as warnings for common pitfalls I've seen the model fall into, improves the performance drastically. Another way to improve the results is an error feedback loop, where the generated Typescript code is transpiled and syntax errors, should they exist, are fed back into the model. This allows the model to correct oversights without requiring the user to explicitly prompt for them. The trace from the Typescript transpiler is usually enough to guide the LLM to a solution. A straightforward approach to improving code accuracy is fine-tuning a model on exemplary code, incorporating past conversations into the training data. This helps reduce common hallucinations and mistakes. Like all LLM-driven products, it's crucial to collect output data early. Once sufficient training data accumulates, it becomes a valuable source for improvement. Most interestingly though, since available functions and parameters are already known ahead of code-generation-time, there is also a case here for structured output, where we build a context-free grammar (CFG), to statically define what code for a valid video might look like. OpenAI unfortunately only allows for JSON to be generated this way. I'm currently exploring ways to either map from a given JSON schema back into Typescript. Utilizing open-source models might be an easier path though, since these allow for modification of how tokens are sampled, making more complex rules easy to implement. Either way, this would make it practically impossible for the model to generate syntactically incorrect code. Optimizing rendering speeds was an entirely different challenge. Let's take video-in-video as an example. To show a user-provided video file on the canvas, the standard HTML video element is a good starting point for a first implementation. To flip through the video frame by frame during rendering, we could set its current time to the rendered frame plus the offset of the video start. This approach works, but prompts the browser to re-seek the video from the last keyframe to the provided time stamp, which means redoing a lot of work on each rendered frame. This becomes incredibly slow as a lot of work is done over and over again. Clearly, a different approach is needed. I solved this issue through a custom frame extraction system built on the WebCodecs API. Instead of relying on the browser's implementation, you can process frames sequentially and save your work, significantly reducing computational overhead. Each extracted frame is painted as an image to the canvas. This optimization has yielded performance improvements of up to 100 times in extreme cases, enabling real-time preview capabilities even for complex compositions. The rendering pipeline operates in two modes: Through my work in this space, I've witnessed how video production tools are being fundamentally reshaped by the intersection of programmatic approaches and artificial intelligence. Through exploring context-enriched prompting, error feedback loops and structured output approaches, I've observed significant progress in reliable AI code generation, while the optimized rendering pipeline using WebCodecs API has solved critical performance challenges. Programmatic video creation is at a turning point. The rise of sophisticated AI models presents a key challenge: How can we preserve the power of code-based approaches while making them accessible to creators across all skill levels? The technical solutions discussed here lay the groundwork for innovation, suggesting a future where video creation can become not only more capable but also more approachable for everyone. 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