Latest news with #DynamoAI
Times
6 days ago
- Business
- Times
Silicon Valley v Soham Parekh, the man who had too many jobs
Silicon Valley has long been built on a complex lattice of productivity targets and ambitious hiring metrics, encouraging a generation of workers to embrace a so-called 'grindset' mentality. The hustlers' culture appears to have limits, however, as start-up founders lined up this weekend to call out an Indian software engineer for working with at least four tech companies at once. 'There's a guy named Soham Parekh (in India) who works at 3-4 start-ups at the same time … beware,' Suhail Doshi, the founder of the software company Playground AI, said on X. '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.' The post was liked more than 250,000 times. • How I juggle three jobs at once: the rise of 'fractional workers' Founders of other Silicon Valley companies such as Dynamo AI, Synthesia, Alan AI and GitHub also accused Parekh of working multiple jobs. Haz Hubble said his social media company, Pally, offered him a package worth $250,000 a year, before withdrawing the offer when Parekh refused to relocate to San Francisco. Hubble told the US broadcaster CNBC: 'Now we know why!' Matthew Parkhurst, founder of the financial software start-up Antimetal, said on X that his company 'realised pretty quickly that [Parekh] was working at multiple companies and let him go'. Parekh, from Mumbai, has admitted the deceit, telling the technology programme TBPN: 'I'm not proud of what I've done and don't endorse it either.' He juggled roles by working for 12-14 hours every day. The motivation, he said, was that he needed money to clear debts. He did not say how these were incurred. 'No one really likes to work 140 hours a week, but I had to do this out of necessity. I was in extremely dire financial circumstances,' he said. His lifestyle turned him into a 'serial non-sleeper', he added. Parekh is by no means alone, with the rise in remote working since the Covid pandemic blamed for the surge in moonlighting. In India, the practice has increased by 25-30 per cent between 2020 and 2023, according to the recruitment agency Randstad India. It has prompted the use of software to verify applicants' backgrounds and flag any overlapping employment. • The rise of 'polygamous workers' — and efforts to catch them out The background verification firm AuthBridge said 5 per cent of candidates had two jobs, with roughly 90 per cent of cases occurring in the tech sector. In 2022, the IT company Wipro sacked 300 employees who were found to also be working for rival companies. 'This is cheating, plain and simple,' said Rishad Premji, its chairman. The practice is not limited to the private sector. Some teachers in state schools take a salary but rarely turn up as they are busy working in a private school or tutoring. Road sweepers have been known to delegate the job to someone else — usually a relative — while working for a higher salary somewhere else. • Civil servant 'held three full-time jobs simultaneously' Despite his dishonesty, Parekh said he 'cared deeply' about the companies he had inveigled and denied having a team of junior software developers to help him cope with the workload, saying he had no funds to hire anyone. Parekh's exposure has done his career no harm. He said he was working for a new company and wanted to focus on it exclusively, but admitted he had still not resolved his financial problems. He said he may yet approach his current employer to ask if he could take on another job — but vowed to take a more 'candid' approach.
Gulf Insider
05-07-2025
- 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. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
