Major League Hacking’s Post

Hackathons changed everything for me. It's where I learned to ship under pressure, build interesting projects with new technologies, and met some of the most talented people I have ever seen - it's even how I landed my internship. Super exciting to see Major League Hacking partner with Backboard.io to help more students build real, prod-ready projects. If you're in tech and still haven't been to a hackathon, what are you waiting for?

We’re excited to partner with Major League Hacking (MLH) to bring production-grade AI building blocks to student developers around the world. MLH is integrating Backboard’s stateful API into their hackathons so builders can go beyond stateless demos and ship complex, production-ready AI apps without the engineering tax. What this means for MLH hackers: Free State Management for Life Bring your own API keys and make any of 17,000+ models instantly stateful. Premium Credits & Prizes Access free software credits for advanced memory features, plus exclusive swag and high-value prize pools. Global Impact A new “Best Use of Backboard.io” prize category is rolling out across MLH hackathons, Global Hack Weeks, and a DEV Challenge. As Jonathan Murray, our CSO and Cofounder, shared in MLH’s announcement: "We believe the next wave of world-changing tools will come from this community. We’re making Backboard.io the starting point of the AI stack for MLH builders, ensuring they can scale from weekend tinkering to enterprise-ready deployments without reworking their foundation." This is just the beginning. We can’t wait to see what MLH hackers build with Backboard. Follow the co-founders for more: Rob Imbeault Jonathan Murray

Proud to officially announce our partnership with Major League Hacking. We partnered with MLH because the most important builders in the world are at their events. Not someday. Right now. The thousands of developers who have joined Backboard.io in the last few months made that obvious. They are sharp, they ask the right questions, and they care deeply about things like LLM neutrality and data sovereignty. These are not trends they read about. These are requirements. Our intern cohort, every single one of them, is an MLH event alumni. That says everything. Through this partnership, builders get free access to stateful AI infrastructure across 50 hackathon events, 3 Global Hack Weeks, and a dedicated prize category. The goal is simple: the students winning at MLH this year should be shipping production-ready applications, not weekend experiments. The future titans of this industry are at MLH events. We are grateful to be part of the community that supports them. Rob Imbeault Chris K. Nir Doshi Kevin Wan Seif Otefa Qasim L. Ryushen Tan Erin Tomorri Jasmine Qin Paul Wu Madusha Cooray Muhammad Balawal Safdar Maggie Zhai Alex Barnes 🍁 Megha Kandoi Alex Norman Raif Barbaros

Attention all Major League Hacking followers, hackathon participants, and ambitious builders. I am happy to share that Backboard.io is now an official partner with Major League Hacking. If you’re building anything at all with AI, just like I was at a hackathon powered by MLH pause whatever you’re currently using for memory infrastructure and take a look at Backboard.io just like I did and thanks to MLH I was able to meet Rob Imbeault, Jonathan Murray, Erin Tomorri, Kevin Wan, Qasim L., Muhammad Balawal Safdar, and Ryushen Tan our technical team this summer for Backboard.io. Excited to see what the MLH community builds with it.

I did not find my internships on job boards. I found them at hackathons. Founders saw how I think. That one weekend project became the talking point in interview that followed. MLH partnering with Backboard.io is a big deal because it closes the gap between what students build at hackathons and what companies actually want to hire for. Production-ready AI apps, not throwaway prototypes. If you are early in your career, a hackathon is the fastest ROI on a weekend you will ever get. #MLH #Hackathon #AI #BuildInPublic #Backboard

VentureBeat published a piece this week calling the current state of AI agents "chaos." OpenClaw has 150,000+ GitHub stars and serious security vulnerabilities. Anthropic just cut off subscription access for third-party agents. Palo Alto Networks flagged autonomous agents as a "lethal trifecta" risk. The pattern is clear: the industry is racing to give AI agents more power without solving who decides what those agents should do. This is the problem we've been focused on at PermaShip. Not faster code generation. Not another autonomous bot with full system access. The layer above that. The executive function that evaluates proposals across security, reliability, product, and cost before anything enters the pipeline. When you give specialized agents access to a codebase without coordination, you don't get leverage. You get noise. Or worse, you get a $45,000 token bill and nothing to show for it. The hard problem was never "can AI write code." It's "who decides what gets built, when, and why." PermaShip is an autonomous engineering platform with that executive layer built in. Nexus, the judgment layer at the center, is open source. The full platform runs continuously on your codebase at an autonomy level you control.

PermasShip isn’t agent orchestration. Orchestration assumes you already know what needs doing. Coordination is the harder problem of deciding whether it should be done at all.

We have spent months debating whether AI writes better code than it debugs. I think we have it backwards. Current coding agents are actually better at debugging than writing from scratch. Debugging has clear objectives and reproducible steps. Building a complete product from zero? That is where things fall apart. Prototypes feel fast and satisfying, but maturity exposes edge cases, context limits, and compounding complexity. AI does not escape the laws of large-project maintenance. But there is a sweet spot: creativity-driven, narrowly scoped open source projects. MemPalace, built by Milla Jovovich and engineer Ben Sigman using Claude Code, hit 7,000 GitHub stars in two days and scored 96.6% on LongMemEval, outperforming paid solutions. It is lightweight, local, and MIT-licensed. The lesson: the more focused the problem, the faster AI helps you validate an idea. Then there is the security reality check. Anthropic's leaked model Mythos discovered thousands of zero-days across major operating systems, including a 27-year-old bug in OpenBSD, and can weaponize them. Anthropic is withholding it and sharing access with 45 tech giants to harden defenses first. Before it becomes a spear, let it serve as a shield. Meanwhile, OpenAI's own GPT-5.4 scored 76% on CTF competitions and earned a High Cybersecurity Risk rating. No existing software is safe in front of these capabilities. So how should we build? Slightly ahead of the models. If you design only for today's capabilities, your product will be obsolete at launch. Build the architecture and framework now, test each new model generation against it, and invest heavily when the engine finally arrives. And in case you thought the open-source race was cooling down: Zhipu's GLM-5.1 just went fully open source under MIT, topped GPT-5.4 and Claude Opus 4.6 on SWE-Bench Pro, and raised prices by 10% while everyone else is cutting them. The game is still on. Read the full article: https://lnkd.in/gVPRiU46 #ArtificialIntelligence #CyberSecurity #SoftwareDevelopment #ProductStrategy #OpenSource

https://lnkd.in/dCitVqcD Headline: 4 Generations in 3 Years: Is the "AI Treadmill" finally moving too fast? 🏃♂️💨 NetworkChuck recently released a video titled "I kind of hate AI," and the sentiment is echoing across my entire feed. It’s called "AI Burnout", the anxiety of constant news, the fear of displacement, and the decision paralysis that comes with a landscape changing every 48 hours. But history has a way of calming the nerves. Just like the printing press didn’t end books and the car didn't end transportation, AI isn't deleting jobs. We aren't seeing less work; we're seeing a shift from being "The Creator" to being "The Orchestrator." In my latest blog post, I dive into: The Software Pivot: Why I’ve officially moved away from OpenClaw in favor of Hermes. When things break, you need maintainable Python architecture, not a sea of GitHub issue duplicates. The Generational Leap: We’ve moved past simple chat windows and IDE plugins. We are now entering the 4th generation: The Orchestrator era, where you manage the agentic loops. The Local Revolution: Why the Qwen3.5 27b or Gemma 4 31B model is the current "Goldilocks" model for those of us running local hardware. They outperform old giants like Deepseek R1 while running on consumer-grade GPUs. The pace is exhausting, but if you stop trying to master every tool and start mastering the architecture, the treadmill stops being a threat and starts being a playground. #AI #TechTrends #OpenSource #LLM #Linux #CyberSecurity #Gemma4 #NetworkEngineering

We have been thinking about AI coding agents all wrong. Current agents are actually better at debugging than writing code from scratch. When a problem is clear, reproducible, and verifiable step by step, AI thrives. But asking an agent to build a complete product from zero is where the real challenge begins. Prototypes feel fast and magical, yet turning them into mature products still requires time, real user pressure, and deep human judgment. The laws of software engineering still apply. That said, AI is exceptionally powerful for focused, creativity-driven projects. Take MemPalace, the open-source memory system built with Claude Code that hit over 7,000 GitHub stars in just 48 hours. It outperformed paid alternatives on the LongMemEval benchmark while running entirely local with a simple ChromaDB and SQLite stack. The lesson: when the problem is narrow and idea-driven, AI can help you validate and ship at remarkable speed. Then there is the security elephant in the room. Anthropic's Mythos model has discovered thousands of zero-day vulnerabilities across major operating systems and browsers, including a 27-year-old bug in OpenBSD. It does not just find flaws, it converts them into usable attack vectors. Anthropic is keeping it private and sharing it with roughly 45 major tech companies to harden defenses before it becomes a weapon. The reality is stark: in the face of this capability, no existing software is truly secure. This also reshapes how we should build products. Rather than designing only for today's model capabilities, architecture should stay slightly ahead of the curve. Build the framework now, test each new generation of models against it, and productize when the engine is ready. If you design strictly for what AI can do today, your product will be outdated by launch day. Finally, open source is not slowing down. Zhipu's GLM-5.1 was released under the MIT license with fully open weights, scoring 58.4 on SWE-Bench Pro and surpassing GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro. The race is far from over. Read the full article: https://lnkd.in/gVPRiU46 #ArtificialIntelligence #SoftwareDevelopment #Cybersecurity #ProductDesign #OpenSource