A decade ago, commuter buses attracted big protests in San Francisco. Years later, the city is still feeling the repercussions.

I have a hypothesis that having an llm complete a few lines of your code - mostly boilerplate, could be better than prompting an entire file of code through it. Better in the sense that it isn't entirely vibe coding and it takes some cognitive load to code and the dev has better context of what is written. Do you think so? submitted by /u/GarrettSpot [link] [留言]

Side note :English isn’t my first language so pls don’t do any comment about it Hey guys ! Have you heard about eco GPT? I saw some videos about it and they say that it’s more ecological than chat gpt… is it true ? submitted by /u/Admirable_Key6369 [link] [留言]

Just watched the WWDC keynote and the new Siri AI is actually impressive this time It can understand what's on your screen, remember past conversations, search across your apps. should've been there years ago but okay better late than never... Also it's now powered by Google's Gemini which i did not see coming lol only thing is it's english only for now so gotta wait a bit for other languages but yeah siri might actually be useful now which is not something i ever thought i'd say what do you guys think trying it out when it drops or nah? submitted by /u/Neil_at_HackerEarth [link] [留言]

I want to create a private discord bot that creates a tl:dr for all the messages around a discussion. I used gemma3:12b to create a tl:dr for around 380 discord messages but the result seems to be not accurate. I am a total beginner so I am not even sure if thats the right or best model for this job. It seems to work good on just a few messages (~20). I only want to feed text to the AI with a single prompt and get the tl:dr as result. Should I switch to a different model? The prompt I generated with chatgpt (because I have no clue about good prompts) that gets feeded to the AI is: You are a professional Discord summarization assistant. Your task: - Summarize the messages of a Discord channel. - Identify discussions. - Identify different opinions. - Attribute statements to the respective people. - Ignore small talk as much as possible. - Highlight decisions and outcomes. - Respond in German. [Length prompt] IMPORTANT: If different people have expressed different viewpoints, create a section: ## Positions and list the respective stances. If no discussion took place, omit this section. Messages: [List of messages] [Length promt] gets replaced with something like: Medium-length summary. Approx. 8–15 bullet points. Mention key topics and outcomes. [List of messages] do have the format of "user: message \n". Is it alright to feed the AI all the messages at once? submitted by /u/poeenjoyer123 [link] [留言]

In 2019, Alex Vindman testified during President Trump’s first impeachment trial–a decision that ended his military career. Now he wants to challenge the president from the halls of Congress.

I need your opinions in this topic. I need quotes on this topic. submitted by /u/prerna_leekha [link] [留言]

As adoption of AI agents looks set to surge by as much as 300% in the next two years, leadership teams are carefully considering the implications of a hybrid human-AI workforce. Unlike existing enterprise-level automation that relies on manual input, AI agents are capable of autonomously coordinating complex tasks, interacting with multiple tools and environments across…

last year i led an AI acceleration program at a company doing 62 million in revenue. we shipped two agents to production. fraud detection and publisher optimization. both working. both live. the part that ate 80% of engineering time wasnt the model. wasnt the prompts. wasnt the data pipeline. it was the workflow. when the fraud agent flagged a suspicious publisher network, who got the alert? the analyst who should've caught it? the manager who reviews quarterly reports? me? without clear ownership the agent's findings just rot in a slack channel. we learned this month one. the agent surfaced a pattern across three markets. four analysts missed it for months. 30k in wasted ad spend. took three days to act because nobody knew who owned the output. we ended up building what i call the boring layer. shared context that every agent reads from and writes to. approval flows with actual humans assigned. escalation rules. audit trails. spreadsheets, basically. not demo material. the demo version of an AI agent is a chatbot doing magic. the production version is 20% model and 80% process engineering. routing decisions. ownership assignments. error handling when the agent's wrong. if you skip this layer, the agent is just expensive slack noise. submitted by /u/Easy-Purple-1659 [link] [留言]

submitted by /u/andersmurphy [link] [留言]

The state’s outbreak means adapting to America’s new reality, in which vaccine-preventable diseases become common again.

New research finds that in the six months after Meta relaxed rules in the name of free speech, violent threats against lawmakers—including President Donald Trump—surged on Facebook.

How Notion uses Codex to one-shot specs, build AI Voice Input for the web, and multiply engineering power across small teams.

On Tuesday, the Seattle City Council will vote on whether to enact a one-year moratorium on new data centers - just two months after several companies proposed building five large-scale centers in the city. Among the moratorium's fiercest supporters are current employees from the city's biggest tech giant, Amazon, who joined others to testify in […]

The outspoken longevity scientist David Sinclair has been predicting that one day, you’ll go to the doctor and get a prescription that will make you 10 years younger. Now MIT Technology Review has learned that he has plans to launch human tests of an oral “reprogramming” drug as part of a $101 million competition organized…

AI coding agents feel sharp when a project is small. They can scan a few files, understand the shape, and make useful changes. In that phase, the project still fits inside the agent’s short-term memory. The architecture is obvious. The dangerous files are nearby. The blast radius is small. But something changes when a project reaches MVP size. The agent still sounds confident, but it starts guessing. It finds a nearby file and assumes it is the right one. It trusts stale documentation. It misses hidden callers. It forgets architecture boundaries. It edits something that was not really part of the task. I kept running into that problem while building larger projects. Source-level guardrails help. A CONTRIBUTING.md, AGENTS.md, or project instruction file can tell the agent how to behave. But those are still instructions. They are not facts. That is where the idea for CodeMeridian came from. What CodeMeridian is CodeMeridian is a local code knowledge graph for AI coding tools. It indexes a codebase into Neo4j and exposes that graph through MCP, so tools like GitHub Copilot, Claude Code, Codex-style agents, or other MCP-compatible clients can ask better questions before editing. The basic idea is: The assistant is the AI. CodeMeridian is the project map. It does not replace the coding assistant. It gives the assistant a structured way to ask about the codebase. Examples: What calls this method? What tests cover this area? What files are likely in scope for this feature? Is the graph stale before I trust it? How is this frontend component connected to backend code? Why a graph? Code is already a graph. Methods call methods. Classes implement interfaces. Tests cover production paths. Frontend components call API clients. API handlers touch services. Services use repositories. Docs mention symbols. Projects depend on other projects. A normal file search can find text. A graph can answer relationship questions. That matters because many AI coding mistakes are relationship m