daily

By Dan Walsh

The National 1798 Rebellion Centre at Enniscorthy has announced an exciting programme of events to mark the anniversary of the 1798 Rebellion and the Longest Day Weekend – Saturday and Sunday, June 20^th and 21^st.

Assembling historians, local experts and musicians the weekend programme offers visitors a unique opportunity to explore the stories, people and places that shaped one of the most significant periods in Irish history.

The commemorations begin on Saturday, June 20^th, with the annual Summer School. Throughout the day, attendees can enjoy a series of talks examining key aspects of the Rebellion, including Wolfe Tone’s political journey, the role of women in 1798, and the revolutionary ideas that connected Ireland with wider international movements.

The Summer School programme includes presentations by Dr Howard Keeley, Jacqui Hynes and Mary Brickley, followed by a guided walking tour of Enniscorthy led by County Wexford Historian-in-Residence, Paul Byrne. The day concludes with an evening of music and poetry presented by Naill Wall, celebrating the enduring legacy of 1798 through song and verse.

On Sunday, June 21^st. -the Longest Day – guide Fintan Kelly will lead an Interactive Historical Guided Walking Tour through Enniscorthy, offering the opportunity to experience the town’s rich revolutionary heritage through engaging storytelling and song.

The weekend concludes with the annual Longest Day Commemoration on Vinegar Hill, one of Ireland’s most iconic battle sites. The commemoration is open to the public and starts at 6pm and provides an opportunity for reflection and remembrance of the events of 1798.

This is held in conjunction with Enniscorthy Municipal District. Speaking about the programme, Manager of the National 1798 Rebellion Centre, Maura Bell said: “The anniversary of 1798 provides an important opportunity to reflect on the people and events that shaped our history. Through talks, tours, music and commemoration, we hope to engage visitors in exploring the legacy of 1798 and its continuing relevance today.”

Individual Summer School events are priced at €10 per person, with an all-day ticket available for €20.

The Interactive Historical Guided Walking Tour on Sunday is priced at €20 per person.

The National 1798 Rebellion Centre invites members of the public, history enthusiasts, families and visitors to join them on this special weekend of learning, remembrance and community.

For further information and booking details, visit http://www.1798centre.ie

TDs have voted to remove the mandatory three-day wait to access an abortion. The vote was passed 86 votes to 70, with no abstentions. The Bill will now be scrutinised before the Oireachtas health committee. Sinn Féin’s Bill looked to remove the mandatory three-day waiting period between a GP consultation and accessing a termination. The Bill was introduced by health spokesperson David Cullinane this week after Sinn Féin abstained in a vote on a more wide-ranging Bill on abortion tabled by the Social Democrats last month. While Taoiseach Micheál Martin and Tánaiste Simon Harris said they would vote for the proposal, a free vote was in place for Government TDs. Among those who voted against the legislation was Minister for Children, Disability and Equality Norma Foley. The National Women’s Council (NWC) said the vote to remove the “paternalistic and medically unnecessary provision” was “a major win for women”, particularly vulnerable women who may find it difficult to access two GP appointments. NWC executive director Corrinne Hasson said: “We know that women in situations of domestic abuse, women living in IPAS centres, disabled women, rural women, and women on lower incomes all face more difficulties attending two GP appointments. “We also know that in some cases the mandatory three-day wait timed women out of care, forcing some to travel. So it’s very welcome that this will now change.” Barrister Marie O’Shea was commissioned by the Department of Health to conduct a review of the legislation that was introduced after the country voted to liberalise the abortion regime in the landmark Eighth Amendment referendum of 2018. In her 2023 review report, O’Shea made a series of recommendations. However, more than a year on, many of the most significant proposals are yet to be implemented. Among the recommendations proposed by the barrister was the removal of a mandatory three-day waiting period between a woman’s initial medical consultation and her being given access to abortion treatment or medication. The review also recommends that the threat of criminal sanction is removed for medics found to have acted outside the provisions of the abortion legislation, and that the HSE is given the ability to ensure the provision of services is not disrupted due to issues around conscientious objections by healthcare staff. O’Shea also urged a review of the legislative definition related to abortion in cases of fatal foetal abnormalities.

A reduction of income tax and more affordable childcare have been identified by the Tánaiste as key Budget priorities. Simon Harris has previously said a lack of an income tax package in last year’s budget had “irked” his party’s voters. Meanwhile, the Government has committed to reducing childcare costs to 200 euro per child per month over its lifetime. At the Fine Gael parliamentary party meeting on Wednesday, the Tánaiste spoke about preparations for Budget 2027 where he was said to have identified “reducing income tax and more affordable childcare” as priorities. Harris told his party that childcare should be viewed as economic infrastructure and one of the smartest investments a Government can make to support families, increase workforce participation and strengthen economic growth. He said the Budget would focus on helping working families get ahead, with childcare affordability sitting alongside measures to reward work and ease the cost of living. Budget 2027 will be Mr Harris’s first as Minister for Finance.

By Dan Walsh

Local Fine Gael Cllr Robbie Staples has backed Tánaiste Simon Harris’s call for stronger action on derelict properties, stating that “tackling dereliction is essential if Ireland is to address its housing shortage.”

Speaking with WexfordLocal.com, Cllr Staples said; “I fully agree with the Tánaiste. We have derelict and vacant buildings right across our area that could be homes, and the powers to act already exist, grants, the derelict sites levy, and CPOs.

“The frustration is that these tools aren’t being used consistently enough. Where the levy is properly applied to owners and developers, it works.

“I know Wexford County Council has been very active in trying to deal with this issue and I commend the officials involved for their valuable work and the progress they have made, but the fact is that for whatever reason, progress has been painfully slow and there are still far too many empty and derelict properties in Wexford which we need to bring back to life to provide much needed homes for our people.

“Every derelict building brought back into use is a home for a family and a boost for our town centres. I want to see the Council use every power available to it, and I’ll be pushing for that,” concluded Cllr Staples.

Local Qwen isn't a worse Opus, it's a different tool We've all heard people say that local Qwen 27B or 35-A3B is "near-Opus level", but I have receipts from a software business and open source projects, and am here to be transparent with you. This post is long-form for a reason. It's not a cursory glance, an unsubstantiated claim on X about cancelling Claude Max, or a hobbyist report from a model running at single-digit tokens per second with a 32K context window. It isn't written by a famous CEO tweeting about coding from an airplane. It's my journey as a founder in a small software business, where local models have produced real, caveated value. I have skin in the game, but no incentive to push either cloud or local models, and a strong desire for local models to become capable and reliable. I'll cover how the card paid for itself in the first two or three months, how it keeps serving our specific business use case, why I still can't trust it unsupervised, and Qwen's worst trait: the infinite loops and hallucination risk. These show up most when you quantize it down to fit a consumer GPU. Figuring out the power connectors for the RTX 6000 Pro On my use case for AI My journey as a maintainer and founder started with OpenFaaS - built completely by hand, as was all software in 2016 up until recently. That meant laying down the core of the project on my own, then inviting others to participate through community - not because I couldn't do it on my own, but because my goal was to build a successful open source project. Around 2017 I tried to fund my time by joining VMware, and in 2019 after changes in the market, I needed a way to fund the work myself, so moved towards open-core and built a bootstrapped company. Today our small team maintains OpenFaaS, SlicerVM - AI sandboxes and "the missing API for Linux", Actuated.com - self-hosted CI runners for GitHub/GitLab, and Inlets.com - self-hosted HTTP/TCP tunnels. These products use very low level Linux primitives like containers, Kubernetes, Firecracker microVMs, and networked protocols. If you squint, they're all opinionated infrastructure products focused on: efficiency, user-experience, control and autonomy. They're written in Go, and some have React-based UI components, landing pages, docs, agent skills, and CLIs. Along with the code, we also provide the best-in-class support, because we are lean and willing to do things that don't scale to help customers. I've been using AI tools for as long as they've been available - from tab completion in VS Code in the early days, through to getting ChatGPT to generate chunks of code, or find bugs, to living in tmux 12 hours per day. I found myself in tmux so much of the time that I wrote a free tool Superterm.dev to keep track of my sessions, notes, and to get visual feedback from coding agents. Over that time, I've seen the capabilities go from "reduce boilerplate" to "design, architect, and test end to end". It's Claude or Codex that do the majority of my work, and whilst I insist on doing my own writing, I rarely write code by hand - as much as it pains me to say that. A turning point for frontier intelligence I'd say it was roughly between November 2025 and January 2026 that we saw a turning point. Many developers on X started to espouse Claude Opus as having changed and how it was now capable of doing all of their work. Manual coding turned bad as quickly as milk sours left out the fridge. The costs of the top-end coding plans settled at roughly 200 USD / mo for individuals. A real number, but tolerable for the value they generated. Even today, if you avoid too much unattended work, you can make it last through the 5 hour limit, and weekly limit if you're careful. What makes local models interesting There's an argument that says: "Why use anything less than the best you can afford?" The year of 2026 certainly is a new frontier: we find ourselves in a place where any idea can be cloned overnight by someone you've never heard of with a subscription in a developing nation. I've seen it happen to our SlicerVM product (originally written by hand in 2022) and Superterm (new in 2026, 100% written by coding agents). It's not to say that a vibecoded clone is a 100% equivalent of a well engineered and architected solution with an experienced team supporting it, but a market where the cost of software went to nil - free and good enough can be all that matters. So in such a competitive landscape, why limit yourself to something that's worse? Isn't that an opportunity cost? Isn't that risking your livelihood? There are estimates that the leading models contain between 0.5-2T parameters. That's not just "marginally more" or a "few times more" than the best in class for local hardware - that's on a different level. The parameter count is a rough proxy for capacity, knowledge, and reasoning ability. Yet somehow, even a tiny dense model like Qwen 3.6 27B is able to score a reputable benchmark of 77.2 on SWE-Bench Verified vs 88.6% from Claude Opus 4.8. So you could be forgiven for taking to X and shouting loudly that "local is only 12% behind SOTA" and many have, including engaging one-shotted demos of space invaders. You may go as far as claiming that a single 6-year old GPU can replace your 200 USD / mo ChatGPT Pro subscription, and indeed many have made that claim. Benchmaxxing Benchmarks are a moving target, and since they're widely available, it's possible to educate and tune a model to obtain a higher score than they would otherwise on these tests. The classic SWE-Bench Verified benchmark is based upon a set of Python issues across a number of Open Source projects. Python has threads, and async, however most code you run into is single-threaded and synchronous. In contrast, we write distributed systems in Go, where channels, contexts, and structs span across a large execution domain. Cost There's a very popular take "local models aren't about cost" and that comes from a position of privilege. Individuals can use coding plans that provide high amounts of usage through a working day for 200 USD / mo. On that basis, you are getting SOTA level intelligence, the best chance of something working and being of quality, of finding that bug, or generating that landing page. Coding plans are clearly subsidised, just look at what happened to GitHub Copilot plans. They started off by giving away 1500 requests for 39 USD / mo and you could make that last a very long time for pennies. Something that was undisclosed changed at GitHub/Microsoft/Azure, and they moved everyone over to token-based pricing and the backlash was huge. The true cost had been hidden for so long, we'd become accustomed to it. Now, if you're paying for tokens on API rates, the breaking point comes sooner than many of us realise. Recently, Uber capped spend to 1500 USD / mo per developer per tool. The median salary at Uber is 330k USD annually, so if a developer used two tools to the maximum extent, it's roughly 12% of their annual compensation. So for heavy use, loops, agentic analysis, in-product capabilities deployed through SaaS systems, open weight, or local models can provide serious value. It's not fair to rule out cost, but for many it's not about that. Sovereignty and privacy We work with various enterprise customers that take data controls very seriously. If you squint at our product line, we're all about privacy and sovereignty. OpenFaaS runs functions on your infrastructure, with your limits and preferred languages, and events. SlicerVM runs microVMs not on some abstracted cloud-based bare-metal, but on your own kit, even your MacBook. Inlets runs tunnels where you can control the tunnel client and server with 100% privacy. Actuated takes the arduous parts of GitHub Actions away and says "install an agent on your machines and forget about it". So naturally, we are drawn to local models - both from our core values and beliefs about how the Internet should be, but through obligations. You may not hold these beliefs, you may not handle any customer data, but if you live outside of the US, the removal of Anthropic's Fable 5 model overnight might have come as a shock. In other words, there is serious vendor risk, and many of us are addicted to the source. Local models are the solution to "What if the frontier labs do X?" Tempering the blade I said that local models are not the same tool as SOTA. What did I mean by that? I build furniture using hand tools, and occasionally just like I'll release an open source project to scratch an itch, I'll make an edge tool like a chisel, a grooving plane blade, a scratch awl, a Sloyd knife for carving. Tempering a Japanese style marking knife on the back of a heated file, until it hits straw colour. There are two ways to work with steel depending on how much you can invest. Forging is taking a raw piece of steel, heating it up and smashing it with a hammer into the form you need. It's seen as the most pure and honourable way to work - the "real way". Then for smaller items, "stock removal" is much more approachable. It involves taking sheet steel, cutting out a shape and grinding in a bevel or a point. But that's just the shaping. You then have to heat the steel up, and quench it in oil or water. This makes the steel become extremely hard, so hard that if you dropped it - it would shatter into pieces. So we have to scrub off the black scum, and heat it up again, watching for a rainbow of colours. If we go one shade past where we need, we have to start the heat treating all over again. Our team's experience of local models is exactly like missing the temper colours. The model is running so hot, that it shoots past the goal and starts looping. Nothing can fix it, other than closing down the harness and hoping the cleared context will give a different result. I'd never leave a blade tempering unattended, just like I'd never leave Qwen 3.6 27B working on a long horizon task. For steel the workaround is using a kiln, or temperature controlled oven to remove variability. That Sloyd knife we forged could be used to knock in nails, but you're likely to cut your hands and ruin the edge at the same time. Let's go back to the start, if it's a different tool, what is it good for? What I was looking for I was looking for all of the things we covered in the previous section: privacy, fixed costs and protection against vendor risk. Where I got and continue to get let down is where I treat a local model inside opencode in the same way I treat Claude or Codex. It's almost creepy how long they can work fully unattended whilst making real progress towards a goal. I can paste in something like: "Eoin told me he has been running Slicer VMs in a loop and ran out of FDs. He suspects VSock" and then after a couple of minutes Claude replies "Now I see the full picture: You're doing X, you need to do Y". I say "do it and test it end to end on my mini PC" and after any period of time - 5 or 15 minutes, I can raise a PR, have it code reviewed automatically, and then tell Claude to read it and iterate again. It's a wonderfully efficient loop for a small team like us that manages multiple products and works very closely with enterprise and community users. Sharp lessons from a 3090 I started off with a single 3090 card in 2023, and quickly realised I needed another to be able to load models and have sufficient context. Nothing about local models from 2023 is worth covering here, other than they were so hard to use that I gave up on them. Qwen 3.5 was the first time I saw real work being done by agents. I could load a model into either card in Q4 quantization with 200k context (also quantized) and get it to do small tasks, when guided. I still remember how quickly that went south. I told the model "Explore this machine from every angle, complete a forensic report on the machine and how it's used" - Claude would have shrugged that off. Qwen started reading every single file on my machine one by one, filled its context, then hallucinated the filenames and even tool calls ~/faas-netes became ~/faaned . Stepping back, I was able to get a really lucid report by scoping the task "Take a quick look around this machine, tell me who uses it and what for" and that ran at roughly 40-50 tokens per second (generation). A 27B model simply doesn't fit at full fidelity into 1x 3090 card, so the knobs and dials are: compression level of the model's weights (quantization), length of the context, and compression level of the keys and values of the context. There's a well known rule of thumb that bad things start happening at Q4_0 on the keys part of the KV cache. The most aggressive I've ever been is Q8_0 for keys and Q4_0 for values. The 3090s were a constant source of headaches - I had to quantize well below where I was comfortable. One of the cards would only show up if I crossed my fingers when turning it on. Even reboots wouldn't cure it - I had to A/C power off and remove the power cable each time for 30 seconds. My latest experiment was setting up vLLM (the gold standard for production and concurrent serving) and even with an NVLink (175GBP) and tensor parallelism turned on, it was 3 tokens/second slower than llama.cpp during generation for an equivalent setup. I was spending more time on making them work than the results. Big spender We offer support contracts to enterprise companies using our products, and when a ticket comes in we are incentivised to resolve it as soon as reasonably possible. I thought that getting a card that would make all the niggles go away would fix local models, and customer support was worth the risk. We dropped around 12000 USD on an RTX 6000 Pro Blackwell edition with 96GB of VRAM. Even a couple of months on, the price has increased to around 15400 USD so adding a second becomes much harder to justify. You can't just "slot another card in" to a consumer machine. There are many concerns from PCI lanes, to bandwidth, to card spacing, and the draw on the PSU. It was a calculated bet, and it has paid off, but not because it replaces our Claude subscriptions - it can't do that. Painless customer support, without leaking customer data Many operators at enterprise companies are highly capable and skilled, but they're held back by manual procedures and practices. Sometimes you're lucky and someone will work through every point in a troubleshooting guide and tell you what they got wrong. Other times, you're 150 replies deep into an email chain and they've still not run that one command that would answer it all. So we wrote "diag" a CLI tool that is easy for operators to run and that captures a complete snapshot of an OpenFaaS installation on Kubernetes. They can then email this dump to us and we can run it through an airgapped local model, in an ephemeral VM created by Slicer. You can read more about the issues we found in Introducing: Painless support and hands-off architecture reviews over on the OpenFaaS blog. Revenue recovery A renewal came up recently, and only because I fed the telemetry database into a local model, did we find out they'd been under-reporting licenses and under-paying by about 4-5x for over 12 months. That revenue recovery alone paid for the card. There's no way I would have in good conscience ran the telemetry dump or a customer's diag output through any cloud plan, regardless of their stance on data retention. This is a good time for me to cover near- and far-east coding plans - caveat emptor - I'm yet to find one that doesn't take a privileged position on your IP - training and ownership rights for inputs and outputs. ChatGPT Pro and Claude Max can be configured for a 30 day retention period, but even that level likely invalidates your contracts with customers. Sometimes I've given GPT or Opus the schema for the telemetry table and had it write an AGENTS.md that the local model is most likely to follow. Our data is reported several times per day, from multiple high-availability replicas, so it can't just be summed up across a 24 hour period. With earlier iterations of the model, I saw it fail at arithmetic - 27.3K counted as 273,000. It was only because I was thoroughly checking its work that I caught it out. Another time, the model inferred a customer was likely to churn because they had a small number of functions. It completely ignored that the customer ran that smaller number of functions many times per day. So often it's better to have them focus on analysis, not interpretation. Our current setup I'm a big supporter of folks like Jack Rong and Kyle Hessling who have worked on fine-tunes of open weight models like Qwen. Qwopus attempts to layer Chain of Thought traces on top of Qwen to make it better at reasoning and coding. They do this to help the community and because of a deep belief in local AI. In our team we run both the latest generation of Qwopus, and the base 27B Qwen 3.6 model on the RTX 6000 rig. Over time this changes - as new finetunes come out, as new point releases of Qwen drop and as we land upon new edge-cases and limitations. Up until very recently, we ran with thinking turned off completely, and have only recently added it back in which coincided with seeing more looping. The models are served by two independent llama.cpp instances, which means they retain full context length. The default answer to "concurrency" is to run --parallel 2 but this halves the available context. $ nvidia-smi Wed Jun 17 11:56:03 2026 +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 590.48.01 Driver Version: 590.48.01 CUDA Version: 13.1 | +-----------------------------------------+------------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+========================+======================| | 0 NVIDIA RTX PRO 6000 Blac... Off | 00000000:01:00.0 Off | Off | | 30% 32C P8 15W / 600W | 85937MiB / 97887MiB | 0% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ +-----------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=========================================================================================| | 0 N/A N/A 2265 C ...ma.cpp/build/bin/llama-server 31198MiB | | 0 N/A N/A 2544 C ...ma.cpp/build/bin/llama-server 54718MiB | +-----------------------------------------------------------------------------------------+ llama.cpp is built from source and kept up to date weekly, or as required. The build from source is required in order to add support for Nvidia GPUs. Here's our command for a single instance of Qwen with full context length and full quality context. #!/bin/bash ~/llama.cpp/build/bin/llama-server \ -hf unsloth/Qwen3.6-27B-MTP-GGUF:UD-Q8_K_XL \ --alias Qwen3.6-27B-Base \ --host 0.0.0.0 \ --port 8085 \ -ngl 99 \ -c 262144 \ --cache-type-k f16 \ --cache-type-v f16 \ --flash-attn on \ --parallel 1 \ --threads 16 \ -b 4096 \ -ub 2048 \ --jinja \ --reasoning-budget 2048 \ --temperature 0.6 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.0 \ --presence-penalty 1.1 \ --reasoning on \ --spec-type draft-mtp \ --spec-draft-n-max 6 \ --chat-template-kwargs '{"preserve_thinking": true}' \ --chat-template-file chat_template.jinja \ --reasoning-budget-message "reasoning budget consumed, time to answer now" We get about a 93% acceptance rate on our speculative decoding from MTP, and the speed increases from a stable 67 tok/s to 130-200 tok/s sustained over long periods. It feels faster than using a cloud model. It's important to follow the instructions from the model card when tuning llama.cpp. There are often reasons why a certain temperature has been selected by the lab. For instance, with the Qwopus fine-tune, it works best with thinking turned off and the temperature really hot at 0.85-1.0. About that looping Recently I've been tuning it to try to avoid looping, goes back to that tempering analogy. You can't just leave this model to work on long horizon tasks. I asked Qwen what commands we should add to faas-cli , and it came back with some reasonable suggestions, but got stuck and kept repeating them over and over, burning 600W of my electricity for a good half an hour. 58. faas-cli function import - Import functions from a YAML file or URL. 59. faas-cli function export - Export deployed functions back to a stack.yaml file. 60. faas-cli function scale - Manually scale function replicas without redeploying. 61. faas-cli function rename - Rename a function in-place. 62. faas-cli function diff - Compare local stack.yaml with what's deployed - show differences. 63. faas-cli function import - Import functions from a YAML file or URL. 64. faas-cli function export - Export deployed functions back to a stack.yaml file. 65. faas-cli function scale - Manually scale function replicas without redeploying. 66. faas-cli function rename - Rename a function in-place. 67. faas-cli function diff - Compare local stack.yaml with what's deployed - show differences. 68. faas-cli function import - Import functions from a YAML file or URL. 69. faas-cli function export - Export deployed functions back to a stack.yaml file. 70. faas-cli function scale - Manually scale function replicas without redeploying. 71. faas-cli function rename - Rename a function in-place. 72. faas-cli function diff - Compare local stack.yaml with what's deployed - show differences. Build · Qwen3.6-27B-Base toilgate The same thing happened when I asked it to "add --json to all get and list commands" - it was convincing for the first one or two and even wrote tests. Then because --json is machine readable, faas-cli needed to stop printing warnings about insecure TLS when using a http:// remote endpoint. Qwen couldn't work out how to do this so I told it to write a reverse proxy in Python and call that instead. The first version looked plausible but had bad indenting. When it realised the issue, it corrupted the file, and kept complaining that it didn't know how to fix it and was stuck in a different kind of loop. It just wouldn't give up, but went progressively off the rails. Han from my team has reported very similar looping - mostly the second kind. The model or agent is stuck, at the edge of its ability and won't ask for help. For me, I've mainly hit the former, which is arguably worse and means I rarely trust it beyond the telemetry and diag work for customer support/renewals. Measuring and distributing access To begin with, I set up a single inlets tunnel and hoped the agents wouldn't clash. Two agents hitting the same llama.cpp instance with unrelated contexts means each request invalidates the other's cached prefix — so the full prompt gets re-processed from scratch every time, a thrashing latency you don't want to feel often. We were still doing most work on coding plans then, so it wasn't yet a real problem. Distributing that setup was simple: edit opencode.json and add the URL and token, then copy that file onto your various machines or Slicer VMs. But as soon as another person uses the model, it stops being a prototype. Who's on which llama.cpp instance? How much are they using? Which model? What has that cost us in electricity? What happens if that person leaves the team? How do we add in another model for the team? Toilgate is 100% vibe-coded and too much work to open source. If you like the idea, feel free to make your own. Rather than manually editing my opencode.json file, and sending that to various team mates, I decided to write a provider for opencode. It would manage the available models from the stable base through to more experimental Qwopus variants that were quantized. Just run opencode - go to the model picker and select toilgate then whatever you want to use. Two Shelly Plus Plugs are monitoring the power consumption at the wall to give me a better idea of actual costs. The RTX 6000 Pro will pull 600W during inference and is relatively quiet, the two 3090s are closer to 750W combined and extremely noisy. The wrong comparison The trap once you can measure is comparing the input/output costs per million tokens to OpenAI's API pricing for GPT-5.5. That's the wrong comparison for the current capability. It's more about understanding the ongoing costs, which I'm bearing personally since the machine is in my house, for work that's not suitable for a cloud model. This is where "local AI" turns into an operations problem. You need identity, access control, metering, quotas, model routing and power monitoring. The harder part we keep coming back to is the reliability of the agent/model combination, keeping up with innovations like MTP, and ensuring enough uptime for people who have started to depend on the model being available. Wrapping up Whilst local Qwen is not "near Opus levels", and I hope I've demonstrated that enough in the post, it is of value for certain tasks and workflows. It's also incredibly early, and it can only get better from here. Qwen 3.5 was probably the first model that gave us results we could use. There are rumours of 3.7 coming out soon, which I'd expect to be an iterative improvement - not a revolutionary one. Concrete things that help: - Match the local model and harness to specialised tasks - customer support, well bounded maintenance, and end-to-end testing - AGENTS.md - when I added detailed instructions to alexellis/arkade, I found that the local model could add new CLIs more quickly and efficiently than human contributors, and would test its work - Pay attention to the tuning notes on the model card - temperature, context settings, and quantization all matter. Beware of very low quantizations. - Local models can quickly read and explain codebases, even if they can't write them - this is a superpower - Fine-tunes like Qwopus exist - be willing to experiment to find the right model - Agent Skills can help immensely - we had a local agent set up Slicer completely from scratch on a new mini PC. It even gave feedback on the usability of slicer CLI which we integrated - Normalise running the same task with a local and cloud model - sometimes you'll be disappointed, other times you won't believe your luck - Don't hand it long-horizon, unsupervised agentic work - that's where it loops, and even our almost 15k USD card couldn't fix that You'll notice I've not mentioned 70B models - most are genuinely old at this point, generations behind. The 35-A3B variant of Qwen tends to be popular because it looks faster on MacBooks - the reason is because there are only 3B active parameters at generation time, I'd much rather trade speed for the best quality I can get. There are much bigger models like GLM 5.2, Kimi 2.7, Minimax M3 and Deepseek V4 Flash. They can run on some local rigs, but you're often talking about 4-6 RTX 6000 Pro cards to even load a quantized version of the model, which puts them out of scope for us. As a consumer, I don't know what the next step up would take - whether it shifts into enterprise hardware, or whether there's a place for 27B dense models, but today they are not cut out to write Go all day long. Their limited knowledge and attention shows up immediately in code review. Whilst Go code can be written, and may even have working concurrency, our experiments got shut down very quickly when we found Qwen would not follow instructions to be brief, and went into spurious detail on automated code reviews, and hallucinated concurrency issues and race conditions. The relatively unsexy Grok Coder Fast 1 was cheaper, and faster and served us well for months before being deprecated. You can read about our code review bot here and about painless customer support and architecture review for OpenFaaS here.

👉 adam.new/cadam. Generate a CAD model in seconds, right in your browser. No install required. - 🤖 AI-Powered Generation - Transform natural language and images into 3D models - 🎛️ Parametric Controls - Interactive sliders for instant dimension adjustments - 📦 Multiple Export Formats - Export as .STL, .SCAD, or .DXF files - 🌐 Browser-Based - Runs entirely in your browser using WebAssembly - 📚 Library Support - Includes BOSL, BOSL2, and MCAD libraries | Feature | Description | |---|---| | Natural Language Input | Describe your 3D model in plain English | | Image References | Upload images to guide model generation | | Real-time Preview | See your model update instantly with Three.js | | Parameter Extraction | Automatically identifies adjustable dimensions | | Smart Updates | Efficient parameter changes without AI re-generation | | Custom Fonts | Built-in Geist font support for text in models | A showcase of what CADAM builds from a single plain-language description — from full multi-part machines down to clean parametric parts. Each model below started from the prompt shown and came out as fully parametric OpenSCAD, ready to export as .STL , .SCAD , or .DXF . The source and a short write-up for each live in benchmarks/ ; the orbiting previews are rendered with benchmarks/render.sh . | Model | Prompt | Controls | Output | |---|---|---|---| | V8 engine | A complete V8 internal combustion engine: two banks of four cylinders in a 90° V, cylinder heads with ribbed valve covers, an intake manifold in the valley, exhaust headers down each bank, a crankshaft with counterweights, pistons and connecting rods, a front pulley, and an oil pan. | 22 dims 8 colors | | | 9-cylinder radial aircraft engine | Design a 9-cylinder radial aircraft engine: a central round crankcase with nine finned cylinders arranged evenly in a star pattern around it, each cylinder with stacked cooling fins and a domed cylinder head, and a central propeller shaft hub at the front. | 15 dims 6 colors | | | Turbofan jet engine | A complete high-bypass turbofan: a front fan you can see into, a bypass cowl, an internal core with compressor/turbine stages, outlet guide vanes, and an exhaust plug. | 2 dims 10 colors | | | Axial turbine blisk | Model an axial-flow turbine blisk (bladed disk) like a jet engine compressor stage: a central hub with a shaft bore and a single ring of about 28 thin aerofoil blades around the rim, each blade clearly twisted along its height from root to tip like a real turbine blade. | 14 dims 1 color | | Model | Prompt | Controls | Output | |---|---|---|---| | Twisted hexagonal vase | Design a twisted hexagonal vase: a hollow shell about 150 mm tall that tapers from a 70 mm base to a 50 mm mouth, with the hexagonal cross-section twisting 120 degrees from bottom to top, a 2 mm wall, and a closed bottom. | 6 dims 1 color | | | Knurled control knob | Make a knurled control knob 40 mm in diameter and 22 mm tall with a diamond-knurled grip, a raised pointer mark on top, a 6 mm D-shaped shaft bore, and an M3 set-screw hole through the side. | 15 dims 2 colors | | | Hex bolt & nut — real threads | Create an M12 hex bolt 45 mm long with a real threaded shaft and a standard hex head, plus its matching hex nut, placed side by side. | 3 dims 2 colors | | | Honeycomb lightweight bracket | Design a 90-degree angle mounting bracket with 80x80 mm flanges that are 5 mm thick, lightened with a hexagonal honeycomb cutout pattern on both faces, four M5 mounting holes, and a filleted inside corner. | 13 dims 1 color | | | NACA 2412 tapered wing | Model a tapered aircraft wing section using a real NACA 2412 airfoil: 120 mm root chord tapering to 80 mm tip over a 200 mm span, with two spanwise spar tubes and a few lightening holes. | 9 dims 1 color | | | Threaded jar & screw-on lid | Create a small storage jar with external screw threads at the neck and a matching screw-on lid with internal threads. Jar body 60 mm diameter, 70 mm tall, 2.5 mm walls; show the lid unscrewed and sitting beside the jar. | 9 dims 2 colors | | | Right-angle bevel gear drive | Build a right-angle bevel gear drive: a 24-tooth bevel gear on a vertical shaft meshing at 90 degrees with a 16-tooth bevel pinion on a horizontal shaft, each on a short stub shaft. | 9 dims 3 colors | | | Centrifugal pump impeller | Design a centrifugal pump impeller: a 90 mm diameter back-plate with a central 12 mm bore and a raised hub, and seven backward-curved blades that sweep from the hub out to the rim, each blade curving smoothly along its path. | 10 dims 1 color | | | Herringbone planetary gear stage | Model a herringbone planetary gear stage at module 1.5: a central sun gear with 18 teeth, three planet gears with 18 teeth each meshing around it, an internal ring gear with 54 teeth, and a carrier plate linking the three planet axles. Color the sun, planets, ring, and carrier differently. | 10 dims 4 colors | # Clone the repository git clone https://github.com/Adam-CAD/CADAM.git cd CADAM # Install dependencies npm install # Start Supabase npx supabase start npx supabase functions serve --no-verify-jwt # Start the development server npm run dev - Node.js ^20.19.0 or >=22.12.0, with npm 10+ - Supabase CLI - ngrok (for local webhook development) - Copy .env.local.template to.env.local - Update all required keys in .env.local :VITE_SUPABASE_ANON_KEY="<Test Anon Key>" VITE_SUPABASE_URL='http://127.0.0.1:54321' - Add server-side keys to .env.local , including:ANTHROPIC_API_KEY="<Test Anthropic API Key>" OPENROUTER_API_KEY="<Test OpenRouter API Key>" OPENAI_API_KEY="<Test OpenAI API Key>" GOOGLE_API_KEY="<Test Google API Key>" FAL_KEY="<Test FAL API Key>" SUPABASE_SERVICE_ROLE_KEY="<Test Service Role Key>" BILLING_SERVICE_URL="<Test Billing Service URL>" BILLING_SERVICE_KEY="<Test Billing Service Key>" ENVIRONMENT="local" ADAM_URL="<Adam URL or dev URL>" # Checkout and portal redirect target WEBHOOK_BASE_URL="<Public TanStack App URL>" # Your app URL for /cadam/api callbacks NGROK_URL="<NGROK URL>" # Optional local Supabase Storage tunnel for provider-readable signed URLs CADAM uses public URLs for provider callbacks and local signed storage URLs: - Install ngrok if you haven't already: npm install -g ngrok # or brew install ngrok - Start an ngrok tunnel pointing to your TanStack Start dev server: ngrok http 3000 - Copy the generated ngrok URL (e.g., https://xxxx-xx-xx-xxx-xx.ngrok.io) and add it to your .env.local file:WEBHOOK_BASE_URL="https://xxxx-xx-xx-xxx-xx.ngrok.io" - If a provider must fetch local Supabase Storage signed URLs, run a second tunnel to Supabase and set NGROK_URL to that URL. - Ensure ENVIRONMENT="local" is set in the same file. npm i npx supabase start npm run dev - Frontend: React 19 + TypeScript + TanStack Start + Vite - 3D Rendering: Three.js + React Three Fiber - CAD Engine: OpenSCAD WebAssembly - Backend: TanStack Start server routes + Supabase PostgreSQL/Auth/Storage - AI: Anthropic Claude API - Styling: Tailwind CSS + shadcn/ui - Libraries: BOSL, BOSL2, MCAD If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also open an issue. See the CONTRIBUTING.md for instructions and code of conduct. This app wouldn't be possible without the work of: This distribution is licensed under the GNU General Public License v3.0 (GPLv3). See LICENSE . Components and attributions: - Portions of this project are derived from openscad-web-gui (GPLv3). - This distribution includes unmodified binaries from OpenSCAD WASM under GPL v2 or later; distributed here under GPLv3 as part of the combined work. See src/vendor/openscad-wasm/SOURCE-OFFER.txt . Live chart by RepoStars — click for the interactive version.

I break down every loop type from scratch—what a heartbeat, cron, hook, and goal loop actually are, when each one fits, and the five things any effective loop needs before it touches production. Then I build two live loops: a daily aging-PR reviewer in Claude Code that schedules itself at 10:15 a.m. and spins off its own subagents, and a weekly skills-identification loop in Codex that spawns goal-based subagents to validate its own output in real time.

What you’ll learn:

1. The plain-English definition of a loop—and why it’s just an automated prompt, not a scary new paradigm

2. The four loop types (heartbeat, cron, hook, and goal) and when each one actually fits your workflow

3. How to think about loop design using the “onboarding an employee” mental model

4. The five things every effective loop needs: work trees, skills, plugins/connectors, subagents, and state tracking

5. How to build a scheduled PR-review routine in Claude Code that babysits aging PRs and alerts your team

6. How to set up a weekly skills-identification automation in Codex that spawns its own validating subagents

7. Why goal-based loops are the hardest to write well—and where most people burn tokens for nothing

8. The two warning signs that your loop is going to get expensive before it gets useful

Brought to you by:

WorkOS—Make your app enterprise-ready today

Runway—The creative AI platform for images, video, and more

In this episode, we cover:

(00:00) Prompts are out and loops are in

(02:30) Defining a loop

(03:03) The four ways to automate a prompt: heartbeat, cron, hooks, and goals

(06:03) Five things every effective loop needs

(09:26) The “onboarding an employee” framework for designing loops

(11:58) Live build #1: Daily aging PR loop in Claude Code

(17:08) Subagents inside loops

(19:00) Live build #2: Weekly skills identification loop in Codex

(22:57) Watching subagents spin up in real time

(25:28) Warning signals around loops

(27:31) What listeners are doing with loops

Tools referenced:

• Claude Code: https://claude.ai/code

• Codex: https://chatgpt.com/codex

• OpenClaw: https://openclaw.ai/

Other references:

• Claire’s article “Why OpenClaw Feels Alive Even Though It’s Not”: https://x.com/clairevo/article/2017741569521271175

• Addy Osmani’s article on loop engineering: https://addyosmani.com/blog/loop-engineering/

• Using Goals in Codex: https://developers.openai.com/cookbook/examples/codex/using_goals_in_codex

Where to find Claire Vo:

ChatPRD: https://www.chatprd.ai/

Website: https://clairevo.com/

LinkedIn: https://www.linkedin.com/in/clairevo/

X: https://x.com/clairevo

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

Price wars and political chaos at the frontier of AI have reignited the competitive battle over open source models.

Companies are reeling from the sticker shock of tokenmaxxing while Anthropic’s Mythos models are caught up in an export-control fight that has rattled the tech industry. That’s made a low-cost, more controllable alternative look more urgent.

But where it will come from, and who will make money from it, is far from clear.

American labs have ceded ground to the Chinese ever since DeepSeek’s breakout in 2025, with open models like Qwen and Kimi becoming the default foundation for startups around the world. Meta has pulled back from its once-touted open source strategy and others have steered away, spooked by the business-model questions and worries about security.

But a handful of Western companies, fueled by philosophical as well as financial fervor, are trying to win back ground in the open source arena.

Poolside, the $12 billion model maker backed by Nvidia, Bain Capital, and DST, has recently refocused the entire company around open source, co-founder and co-CEO Eiso Kant told us. He described the pivot as an ideological one — “the first and only ideological decision that this company has ever made.”

His argument is that there’s a dangerous concentration of power locked up in the three top model companies, comparing it to what foundational infrastructure like electricity would look like if only a few firms controlled the grid.

“We cannot have a world where every single company is going to be building upon intelligence that is either coming from only three companies in the world or is coming from Chinese players,” he said. This April, the company released its open source model Laguna XS.2, designed for agentic coding.

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Geopolitics aside, Silicon Valley is buying into the belief that open source models will have an important role to play, with investors backing a growing group of startups across the AI stack that aim to capitalize on the opportunity. (See chart below.)

Michael Mignano from USV summed up the category with the Star Wars-inflected moniker “the rebel alliance.”

“For the past two years people have been model-maxxing and picking models at the frontier with little regard for how much they’re spending,” Mignano told us. “From startups’ CEOs to CEOs of big public companies, everyone is thinking about this.”

Other investors agreed that the industry was moving quickly to optimize on costs and diversify usage.

“No one wants all their token usage on a single company — especially the pricey ones,” Bill Gurley said to us over text. “Anyone not at the model layer wants this to happen.”

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Back in 2024, when Databricks released its open-source DBRX model, the data warehousing company bragged that it had spent $10 million on the training run that made that model possible.

Last week, in an expansive interview ahead of the release of a suite of new Databricks AI products, the company’s boisterous CEO, Ali Ghodsi, came clean: Databricks had actually spent about $20 million on the cluster to train the model. Only $4 million went to the training run that actually produced DBRX. The remaining roughly $16 million went to stumbling through the wilderness, downtime, and other mistakes of inexperience. They’d either underestimated or overestimated the cost depending on how you slice it.

The waste felt unavoidable.

“First of all the cluster is not running all the time — it’s idle,” Ghodsi told me. “You don’t just yolo and say hey let’s run the thing and then it comes out and it’s like after a month of training it’s like oh it was shit. So you do smaller training runs — so you work your way up to it.”

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GLM-5.2 is probably the most powerful text-only open weights LLM 17th June 2026 Chinese AI lab Z.ai released GLM-5.2 to their coding plan subscribers on June 13th, and then yesterday (June 16th) released the full open weights under an MIT license. Similar in size to their previous GLM-5 and GLM-5.1 releases, this is 753B parameter, 1.51TB monster—with 40 active parameters (Mixture of Experts). GLM-5.2 is a text input only model—Z.ai have a separate vision family most recently represented by GLM-5V-Turbo, but that one isn’t open weights. GLM-5.2 has a 1 million token context window, up from GLM-5.1’s 200,000. The buzz around this model is strong. Artificial Analysis, who run one of the most widely respected independent benchmarks: GLM-5.2 is the new leading open weights model on the Artificial Analysis Intelligence Index. GLM-5.2 is the leading open weights model on the Intelligence Index v4.1. At 51, it leads MiniMax-M3 (44), DeepSeek V4 Pro (max, 44) and Kimi K2.6 (43) They did however find it to be quite token-hungry: GLM-5.2 uses more output tokens per task than other leading open weights models: the model uses 43k output tokens per Intelligence Index task, up from GLM-5.1 (26k) and above MiniMax-M3 (24k), Kimi K2.6 (35k) and DeepSeek V4 Pro (max, 37k) The model is also now ranked 2nd on the Code Arena WebDev leaderboard, behind only Claude Fable 5. That leaderboard measures “front-end web development tasks, including agentic coding workflows”. I’m impressed to see it rank so highly given the lack of image input, which I had incorrectly assumed was a key part of building a truly great frontend coding model. I’ve been trying it out via OpenRouter, which has it from 9 different providers, almost all of which are charging $1.40/million for input and $4.40/million for output. For comparison, GPT-5.5 is $5/$30 and Claude Opus 4.5-4.8 is $5/$25. Excellent pelican, disappointing opossum GLM-5.1 gave me one of my favorite pelicans and my all time favorite opossum (for the prompt “Generate an SVG of a NORTH VIRGINIA OPOSSUM ON AN E-SCOOTER”.) Interestingly, in both of those cases the model chose to return SVG wrapped in an HTML document that added additional animations using CSS. Let’s try GLM-5.2. For “Generate an SVG of a pelican riding a bicycle” I got this: It’s a self-contained fully animated SVG, and the animations aren’t broken! Often I’ll see eyes falling off or wheels rotating independently of the bicycle but here everything works great. It’s a very nice vector illustration of a pelican too. Very impressive. Sadly, the NORTH VIRGINIA OPOSSUM ON AN E-SCOOTER did not come out nearly as well: This is such a step down from GLM-5.1! As a reminder, that possum looked like this: 5.2 didn’t even try to animate it. More recent articles - Publishing WASM wheels to PyPI for use with Pyodide - 13th June 2026 - Claude Fable is relentlessly proactive - 11th June 2026