Back to List
Industry NewsRadioLaunchTechnology

Show HN: Launching a New LPFM Radio Station - KPBJ.fm

The news announces the launch of a new Low-Power FM (LPFM) radio station, KPBJ.fm. The announcement, made on Hacker News, indicates the station is now live, with its official website being KPBJ.fm. Further details regarding programming, location, or specific launch events are not provided in this initial announcement, which primarily serves to inform the public about the station's debut.

Hacker News

The original news content is extremely brief, consisting only of the word 'Comments' and a link to the station's website. Based on the title 'Show HN: I'm launching a LPFM radio station' and the provided URL 'https://www.kpbj.fm/', it can be inferred that a new Low-Power FM (LPFM) radio station named KPBJ.fm has been launched. The 'Show HN' prefix suggests this is an announcement made on Hacker News, a platform often used by developers and entrepreneurs to showcase new projects. The launch date is specified as February 17, 2026. Beyond the existence and launch of the station, no further details such as its programming format, geographical coverage, mission, or the individuals behind the launch are provided in the original information. The 'Comments' section likely refers to a place where users can discuss the announcement on Hacker News.

Related News

Granola Privacy Alert: AI Notes Viewable via Link and Used for Training by Default
Industry News

Granola Privacy Alert: AI Notes Viewable via Link and Used for Training by Default

Users of the AI-powered note-taking application Granola are being advised to review their privacy settings following revelations regarding data accessibility and usage. Although the company markets its service as 'private by default,' the platform currently allows anyone with a specific link to view notes. Furthermore, Granola utilizes user notes for internal AI training purposes unless individuals manually opt out of the process. Positioned as an AI notepad for professionals, these default configurations have raised concerns regarding the actual level of privacy provided to its user base. This report explores the discrepancy between the marketing claims and the functional reality of Granola's data handling policies as reported by The Verge.

OpenAI Expands Media Footprint with Acquisition of Technology Talk Show TBPN
Industry News

OpenAI Expands Media Footprint with Acquisition of Technology Talk Show TBPN

OpenAI has officially acquired the technology talk show TBPN, marking a strategic move into the media and content space. While the acquisition has been confirmed, OpenAI has not disclosed the financial terms of the deal. Furthermore, the future of TBPN’s existing distribution channels remains uncertain, as the company has not yet clarified whether the show will continue its current presence on major platforms including YouTube, X (formerly Twitter), and various podcast networks. This acquisition highlights OpenAI's growing interest in controlling tech-centric narratives and engaging directly with audiences through established media properties, though specific integration plans and the long-term status of the show's accessibility are currently unavailable.

Open Models Reach Parity with Closed Frontier Models in Core AI Agent Tasks and Efficiency
Industry News

Open Models Reach Parity with Closed Frontier Models in Core AI Agent Tasks and Efficiency

A recent evaluation by LangChain reveals that open models, specifically GLM-5 and MiniMax M2.7, have crossed a significant performance threshold. These models now match the capabilities of closed frontier models in critical agent-related functions, including file operations, tool utilization, and instruction following. Beyond performance parity, these open-source alternatives offer substantial advantages in cost-effectiveness and reduced latency. This shift marks a turning point for developers and enterprises looking to deploy sophisticated AI agents without the high overhead typically associated with proprietary closed-source systems. The findings suggest that the gap between open and closed models is closing rapidly in the domain of functional AI tasks.