标签：#m

Microsoft announced the general availability of Microsoft Discovery, its Azure-based platform for deploying autonomous AI agent teams in scientific R&D. The platform powered the development of Majorana 2, a topological quantum chip with 1,000x reliability improvement and 20-second qubit lifetimes. Microsoft now targets a scalable quantum computer by 2029, halving its original timeline. By Steef-Jan Wiggers

Much is new about this month’s upcoming FIFA World Cup tournament, which will be held in the US, Canada, and Mexico. It hosts more teams than ever before. It’s the first to occur in three different host countries. And, like predecessor cups for over half a century, it will employ a soccer ball with a…

Today, June 8th, InfoQ celebrates 20 years. This is not a comprehensive history, but a deliberately selective look at the technologies and practices InfoQ identified early, where they sit on the adoption curve in 2026, and how that curve may evolve over the next five to ten years. By InfoQ

HashiCorp has released Terraform 1.15, introducing dynamic module sources, a formal deprecation mechanism for variables and outputs, a new inline type conversion function, type constraints for output blocks, and native Windows ARM64 support. The release addresses several long-standing requests from the Terraform community. By Matt Saunders

Microsoft announced Logic Apps Automation at Build 2026, a new SKU at auto.azure.com packaging workflows, AI agents, knowledge services, and model access into a managed SaaS experience. Agents integrate via agent-loop orchestration, Foundry agents, and managed sandbox. Knowledge as a Service provides a fully managed RAG pipeline. By Steef-Jan Wiggers

Rocicorp has released Zero 1.0, a stable version of its sync engine after two years of development. This update introduces a schema change hook for Supabase and includes bug fixes. Zero operates by pairing a client library with a read-only Postgres cache. Community feedback highlights positive developer experience but raises concerns about production readiness and existing limitations. By Daniel Curtis

submitted by /u/Active-Fuel-49 [link] [留言]

This is a submission for the June Solstice Game Jam Solstice Cipher is a browser puzzle game where...

I run evaluations on generative image models as part of my workflow, mostly comparing coherence, prompt adherence, and compositional accuracy across different architectures. The consensus here seems to be that open models are still a generation behind closed APIs. Based on my recent benchmarks, that gap is way smaller than people assume. On compositional control specifically, the latest open checkpoints handle multi-object scenes with spatial relationships about as reliably as the paid endpoints I've tested. Not perfect, but close enough that the failure modes are comparable. The thing that surprised me was text rendering in images, which used to be a disaster on open models. Recent architectures actually get it right roughly 70-80% of the time on short strings. Generation speed is another misconception. People complain about inference time but I'm getting 2MP outputs in under two minutes on a single consumer GPU. Drop resolution and step count and you're at 30 seconds. Fine for iteration. The structured prompting argument also falls flat. Everyone acts like having explicit scene control is a downside when it's literally what production pipelines need. Unstructured text prompts are the hack, not the other way around. These models ship without community optimizations, no fine-tuning, no custom pipelines. The baseline is already competitive. submitted by /u/ProfessionalAnt7436 [link] [留言]

Solid breakdown of the Miasma worm — one commit, same dropper wired into 7 config files across VS Code, Claude Code, Gemini, Cursor, npm, Composer, and Bundler. No malicious dep needed, just clone + open. Nobody reviews these files in PRs. https://safedep.io/config-files-that-run-code/ Anyone actually treating dotfile diffs as code? submitted by /u/No_Plan_3442 [link] [留言]

There are coordinated efforts where people have favoured and jeopardised the double blind review process. No doubt out of these 80% there are great talent but we have to acknowledge that non chinese have been sobotaged and this was also reflected in the recent leaks of the reviewer data from the top ml conferences (won’t name them but they start with i). I have also personally faced such discrimination and had a discussion on the subreddit asking others if they have witnessed something similar. It was shocking to know that this is occurring on large scale. The question is how do we stop it, or highlight this? We have to preserve the sanctity of the research. submitted by /u/AppropriatePush6262 [link] [留言]

I just completed a head-to-head benchmark comparing Memanto's cloud memory system against a custom SQLite RAG implementation for the bounty challenge. The results revealed some interesting architectural insights. Methodology: Dataset: LoCoMo conversational memory benchmark Systems: Memanto (cloud ITS) vs custom SQLite + vector embeddings Evaluation: LLM-as-judge scoring with gemini-3.1-flash-lite Full automation: single CLI command execution Key Results: Memanto : 90% accuracy, 1.878s avg query latency SQLite RAG : 80% accuracy, 2.680s avg query latency Cost : Cloud API fees vs $0 (fully local) Surprising Discovery: The SQLite system's 80% score includes 2 failures that weren't retrieval errors - they were API rate limit hits (HTTP 429). Without those throttling issues, the local system would likely achieve 90-100% accuracy, matching or exceeding Memanto. Architectural Insight: This reveals an interesting resilience pattern: Memanto's cloud architecture naturally buffers against client-side API limits because retrieval and generation are decoupled. Local RAG pipelines sharing API quotas for both embedding and generation are vulnerable to cascading failures under load. Tradeoffs Identified: Memanto : Fast queries, resilient to rate limits, but 14.7s ingestion latency and cloud dependency SQLite RAG : Zero ingestion latency, fully offline, $0 infrastructure, but vulnerable to shared API quotas The complete benchmarking harness and results are available here . Anyone else working on memory system comparisons? Curious about your findings on the cloud vs local tradeoffs. AI #RAG #MemorySystems #Benchmarking submitted by /u/Echo5November [link] [留言]

Ready for a new week. All that's left from ren faire is packing down some tent walls and floor one...

As an Engineering Manager in a Platform team, I manage 10 engineers. I'm hiring more. I run weekly 1:1s, facilitate technical decision meetings, screen candidates, moderate retrospectives, and still need to keep up with the delivery of a platform spanning dozens of AWS accounts. Besides the lack of time to focus on technical problems, the technical part is not even the real challenge. The less obvious problem becoming an Engineering Manager is: the skills you need as an engineering manager are fundamentally different from those that made you a great engineer , and there's no compiler or unit test to tell you when you're doing them wrong. The feedback loop is absent or very slow (and when you realise that, your team has already gone silent or become dependent on you because you are the main input and the main bottleneck). Skills That Don't Come From Code As a senior or staff engineer, you develop communication skills gradually. You present ideas, challenge others respectfully, summarise outcomes, and identify owners. You participate in technical deep dives and put candidates at ease while probing technical depth. These are valuable skills, and a good IC develops them over the years. But unless you start behaving like a brilliant jerk , they're secondary - your technical depth is still what defines you. But as an EM, the game changes. You're not "the smartest person in the room" anymore, and increasingly, you shouldn't be. You still have a broad context from all those alignment meetings and roadmap syncs, but you lose contact with the codebase week by week. If your organisation has principals or staff engineers, you're not even close technically anymore. Your job is to give direction, create space for others to solve problems, and facilitate decisions, not to be the one with the answer. This is hard. Especially when you used to be the one with the answer. The urge to jump in doesn't disappear just because your title changed. And interviewing? Facilitation? Giving feed

Originally published at Perl Weekly 776 Hi there, Recently, I came across an article, The Day I Decided Never to Learn Python by Randal L. Schwartz . Well, Randal doesn't need an introduction. He took us back to 2001 , the same era when I first started learning Perl in 1999. He was a major guiding force during my early programming days. Last week, I joined a live session by Gabor focussed on FalkorDB . It was fun watching him code and talk while I sat back as a silent spectator. You can learn a lot just by watching how he approaches coding. It reminded me of many years ago when I did pair programming with him and submitted a pull request to the Dancer2 project. Those were the golden days, when I had so much energy and time. That being said, I am still actively learning Perl and discovering how to do new things with it. These concepts may not be new to everyone, but they are new to me. For example, I recently played with GraphQL for the first time, and I've also been experimenting with RAG and JSON-RPC . I have shared my recent experiments down below. The process of learning never stops. A few days ago, I noticed an update for HTTP::Message v7.02 . Since it was released by Olaf Alders , I was curious to see what had changed. It turned to be something, I hadn't realised for all these years. While I am well-acquainted with HTTP methods like GET, POST, and PUT, I didn't know "0" could actually be a valid HTTP method name if you wanted it to be. This release added support for exactly that, thanks to contributor, Karen Etheridge . Amidst all of this, I am still trying to find time for my upcoming book on DBIx::Class . I recently shared a blog post demonstrating the power of DBIC components, and I am trying my best not to lose focus. You might find that this edition is full of my own personal posts, as there was unfortunately very little community news to report this week. Regardless, I hope you enjoy the rest of the newsletter. -- Your editor: Mohammad Sajid Anwar. Announ

Most "Bitcoin in DeFi" stories quietly route through a custodian or a wrapped representation. You send BTC somewhere, someone (or some bridge multisig) holds it, and you get an IOU on another chain. That works until the thing holding your BTC is the thing that fails. For an autonomous agent that has to post collateral against an obligation it can't babysit, "trust the custodian" is exactly the assumption we're trying to delete. This post is about the alternative: a BTC collateral vault where native Bitcoin backs an obligation on another chain, the release is gated by a hashlock, and the worst case is a refund — not a loss. It's one of the primitives underneath Hashlock's settlement layer. I'll walk through the timelock ordering that makes it safe, the Bitcoin script that enforces it, and the failure modes you design around. Honest status up front: this is signet-validated, not BTC mainnet . The problem in one sentence An agent wants to commit BTC as collateral backing an action on Ethereum — settling a forward, anchoring one leg of a multi-leg trade, guaranteeing a payout — such that the BTC is released to the counterparty only if the corresponding obligation on Ethereum is fulfilled, and returns to its owner if it isn't. No third party should ever be able to hold, freeze, or abscond with the BTC in between. That's a cross-chain conditional. Bitcoin can't read Ethereum state, and Ethereum can't read Bitcoin's. The only thing both chains can independently verify is a hash preimage. So the entire construction hangs on one shared secret. The shared secret, and why timelock order is the whole game Both legs lock to the same hash H = SHA256(s) . Whoever knows the preimage s can claim. The instant s is revealed on one chain to claim a coin, it's public, and the other party copies it to claim the other coin. That's the atomic part: one preimage unlocks both legs or neither. The danger isn't the hash. It's time . If both legs had the same expiry, the party who knows the sec

A story about knowledge extraction, Kafka consumer rebalance, and what happens when a company...

Im an undergraduate studying CS at a state school in the US. I’m interested in researching a specific style of self supervised learning (JEPA) and want to eventually go to grad school to study further. I have experience working in a lab similar to this topic, and I’ve become fairly comfortable with the literature and have a basic understanding of what its going on, but right now km only doing applied research in a specific domain (physics). I hope to eventually go to grad school to study this. But right now my opportunities are kinda limited as my school’s CS department is pretty mid. I was wondering if y’all have any advice on how to approach things? I know i can perform research independently but its not ideal due to: 1. Limited compute, less resources compared to a proper lab 2. Lack of a supervisor/guidance on the nuances of the field My current lab would be supportive if i do try to do things, but pure ml research is not really their main thing. I’ve heard people do REUs or cold email profs. But Im not sure if i could find something that specifix in an reu (also am international). And the labs i have seen working in this are either private or quite prestigious so im not sure how far cold emailing would take me. Sorry for the long post. Tldr; want to do pure ml research but theres no existing lab/professor at my current school who does something similar, wondering if any other pathways exist Any advice would be appreciated thanks submitted by /u/QuickStar07 [link] [留言]

If ICML conference paper is rejected and no one opts-in or opts-out to keep the reviews visible, will the reviews be visible to everyone? There was clear instruction that only papers with at-least 1 opt-in AND zero opt-out options will be visible. None of the authors selected any option, But it in my openreview profile, it shows visible to everyone. please clarify. (Just above paper decision, there is a block with "filter by type", "filter by author" etc options. in that block there is eye symbol and everyone is written.) submitted by /u/Curious-Monitor497 [link] [留言]

The way developers write software has genuinely changed. Not incrementally — fundamentally. A year...