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Dan Walsh homes at Coolballow, Wexford

Minister for Housing, Local Government and Heritage James Browne TD visited the new affordable housing development known as An Lisín Mór ‘The Big Garden’ at Coolballow on the outskirts of Wexford town.

Applications for An Lisín Mór have generated strong interest, reflecting the continued demand for affordable home ownership opportunities in the county.

The development provides 20 high-quality, A-rated homes and forms part of the Council’s wider strategy to support sustainable housing delivery.

Applications for An Lisín Mór, Coolballow have closed.

By Dan Walsh

The Minister for Culture, Communications and Sport, Patrick O’Donovan TD, has announced a funding allocation of €371,792.25 for 105 events to support Small Scale Local Festivals and Summer Schools taking place around Ireland in 2026.

Four Wexford projects have been awarded funding as follows;

The Verida Project, EDITOR’S Note; Never heard of it but research assures me, “it is a not-for-profit project dedicated to reshaping how we understand and support mental health”. €5,000.

Gap Arts Festival, Ballythomas, Gorey, €4,954.

Bunclody Trad Fest (St. Aidan’s Hall), €700.

St. Michael’s Parish Church, Gorey, €2.250.

The scheme is designed to assist local cultural events which may not be eligible under funding criteria for larger scale events such as those supported by Fáilte Ireland, the Arts Council and similar bodies.  

Funding was allocated following a competitive applications process, with a maximum grant of €5,000 available.

Speaking to WexfordLocal.com, Minister Patrick O’Donovan said; “Culture and the arts play a vital role in enriching our society, strengthening communities and preserving Irelands unique heritage.

“Through the support of my Department, festivals and summer schools across the country continue to provide opportunities for people to connect, learn and celebrate our shared traditions.

“I extend my sincere thanks to all involved and encourage everyone to take the opportunity to enjoy the excellent programme of cultural events taking place throughout 2026,”concluded Minister O’Donovan.

The new rules of context engineering for Claude 5 generation models We removed over 80% of Claude Code's system prompt for more advanced models. How to apply the lessons we learned to your own context engineering in Claude Code and with your own agents. But when you send a message to Claude, the prompt is only a small part of the context it gets. Much of your context is assembled from your system prompt, Skills, CLAUDE.md files, memory, and other sources. We call this context engineering, and it makes a big impact on the results you generate when using Claude Code or in building your own agents. Unlike a prompt, context is used generally across many requests, so it cannot be as specific. How do you build these general prompts and guidance for Claude, especially when you don’t know what a user’s prompt might be? This can be surprisingly difficult as Claude’s own capabilities evolve. Most recently, we noticed a large jump in the way we prompt the newest generation of Claude models. We removed over 80% of Claude Code’s system prompt for models like Claude Opus 5 and Claude Fable 5 with no measurable loss on our coding evaluations. Here’s what we’ve learned about prompting this new class of models, and how you can utilize it to update your context engineering. We’ve put these best practices in `claude doctor;` use the command /doctor in Claude Code to rightsize your skills, and CLAUDE.md files. Unhobbling Claude Overall, we found that we were overconstraining Claude Code, both through our system prompt and in our CLAUDE.md files and skills. For example, when we read transcripts of our own internal usage of Claude Code, we see several conflicting messages in a single request like “leave documentation as appropriate,” or “DO NOT add comments” as our system prompt, skills, and user requests clash with each other. Generally, Claude can interpret the user’s intent to get to the right answer, but Claude must think more carefully about these overlapping and conflicting messages before deciding what to do. And while these constraints were once needed to avoid worst case scenarios, we have since found we can delete many of them and let the model use surrounding context and judgement instead. Additionally, Claude Code now has many more tools. Claude used to rely on CLAUDE.md as a source of memory, information, and guidance. Now we have memory, artifacts, and skills, which Claude can use to create new ways of loading and sharing context across sessions. There were a number of previous context engineering best practices that had become myths. Including:. Then: Give Claude rules Now: Let Claude use judgement When we first rolled out Claude Code, we needed to be sure that Claude avoided worst case scenarios, such as deleting files. This meant we would give particularly strong guidance that might not always be true, For example, in the system prompt we used to say: In code: default to writing no comments. Never write multi-paragraph docstrings or multi-line comment blocks — one short line max. Don't create planning, decision, or analysis documents unless the user asks for them — work from conversation context, not intermediate files. But for a certain subset of prompts, this guidance would be wrong. In the case of documentation, the user may have their own preferences, or specific parts of very complex code might need multi-line comment blocks. Still, without these guardrails for older models, the comments Claude wrote would be incorrect in many cases and we had to accept this tradeoff. But newer models have better judgement and can handle these decisions well without explicit rules. In the new system prompt we say: Write code that reads like the surrounding code: match its comment density, naming, and idiom. Then: Give Claude examples Now: Design interfaces The number one rule for tool usage was to give Claude examples on how to use them. With our newest models, we’ve found that giving examples actually constrains them to a certain exploration space. Instead of using examples, think more about the design of your tools, scripts and files- what parameters does Claude have and how can they be more expressive? For example, in the Todo tool example, just listing status as an enumeration between pending, in_progress, and completed, hints to Claude about how to use it. The instruction on keeping one item in_progress helps define our requested behavior. Then: Put it all upfront Now: Use progressive disclosure Because Claude Code was focused on coding, our system prompt included detailed information on how to do code review and verification. These were not always needed, but when they were, it was crucial information. Since then, Claude Code has gotten very competent at using progressive disclosure- loading the right context at the right times. For example, we moved verification and code review into their own skills that Claude Code could selectively call. But progressive disclosure is not just for skills, we also use it for tools. Some of our tools are ‘deferred loading,’ which means the agent must search for their full definitions using ToolSearch before using them. This allows us to have more tools (such as our Task tools) that don’t take up context until they’re needed. The same can be applied to your own CLAUDE.md and Skill.md files. A common myth is that you want to make these a central repository for every known practice that you might run into, because Claude would not find it otherwise. Instead, consider having a tree of files that can be loaded at the right time. Then: Repeat yourself Now: Simple tool descriptions Earlier Claude models could sometimes need repeated instructions or be more likely to listen to instructions at the end of their context window than at the start. This meant our system prompt would sometimes have references to tools in the main system prompt as well as instructions in the tool description. We found we could delete these repeat examples and put instructions on how to use tools in the tool descriptions rather than the system prompt. Then: Memory in CLAUDE.md files Now: Auto-memory We used to encourage users to save things to Claude’s memory, by using the # hotkey to write to their CLAUDE.md automatically. Instead, Claude now automatically saves memories that are relevant to the work and to you. Then: Simple specs Now: Rich references In plan mode, Claude Code has heavily relied on markdown files with plans. Storing these files as plans helped Claude refer to them when needed. Another similar best practice was to store specs in the codebase for Claude to refer to while working across longer projects. But we’ve found that Claude can handle increasingly more complicated references. Instead of simple markdown files, Claude can reference HTML artifacts created by our new artifacts feature. You may also give Claude references in the form of code. A spec may also be a detailed test suite, or a function in a different codebase that Claude might port. Rubrics are another form of references. Rubrics allow Claude to try and verify your taste in a particular field (e.g. what does a good API design look like) by using dynamic workflows and spinning up verifier agents with those rubrics. Applying this to your context Pulling this all together, what does this look like when you assemble your context? System Prompt A system prompt is heavily tied to the product context. It tells Claude what product it’s operating in and what it’s doing. For Claude Code, you will likely never modify this, but if you are building your own agent harness, this is where you should spend a lot of time. CLAUDE.md Keep your CLAUDE.md lightweight and briefly describe what your repo is for, but spend most of the tokens on gotchas inside of the codebase. For example, you may organize your code to keep types in one monolithic file and nowhere else. Avoid stating ‘the obvious’ things Claude should know by looking at your file system or your repo. Use progressive disclosure heavily, for example if you have several unique instructions on how to verify your work, create a verification skill and reference it from your CLAUDE.md. Skills Think of skills as lightweight guides to let Claude find information when needed. Avoid making them overconstrained, except in highly important areas. For long skills, try and use progressive disclosure as much as possible- divide it into many files and split them out. It’s best when skills encode particular opinions, knowledge, or best practices that are particular to you, your team, or product. References You can @ mention files to include them as references. References allow Claude to refer to in-depth information about the current plan. This might be in specs files, mockups, or even entire codebases. Generally you should prefer files that are in code as it provides clear, high-fidelity instructions to Claude in a language it knows very well. For example, a HTML mockup of a design will generally produce better results than a description of the design or a screenshot. Try simplifying Across your system prompt, skills, and CLAUDE.md files, you may need to simplify just like we did. We rolled out a new command called `claude doctor,` which will help you do this automatically as well. For more details on prompting more advanced models specifically, check out our Fable field guide. This article was written by Thariq Shihipar, member of technical staff, Anthropic. FAQ No items found. Related posts Explore more product news and best practices for teams building with Claude. Jul 24, 2026 Claude models explained: choosing the best model for your use case

Open to Work · 𝕏 slvDev · LinkedIn This is a 28.9 million parameter language model that generates text on an ESP32-S3, a microcontroller that costs about $8. It runs on the chip itself, with nothing sent to a server, and it writes each word to a small screen wired to the chip at roughly 9 tokens per second. The last language model people ran on a chip like this had 260 thousand parameters, so this one holds about a hundred times more. It fits because most of the model lives in flash instead of RAM, using an idea from Google's Gemma models called Per-Layer Embeddings. | Parameters | 28.9M stored (25M of them in a flash lookup table) | | Chip | ESP32-S3, about $8, with 512KB SRAM, 8MB PSRAM and 16MB flash | | Speed | about 9.5 tok/s end to end (9.7 tok/s of pure compute) | | Connectivity | none, everything runs on the device | | Model size | 14.9MB at 4-bit | A microcontroller has very little fast memory. The ESP32-S3 gives you 512KB of SRAM. Normally the whole model has to be reachable from there, which keeps you stuck with tiny models, and that is why the previous model on a chip like this had only 260 thousand parameters. The way around it is to stop putting the model in fast memory at all. Most of a language model's parameters sit in an embedding table, which the model reads from rather than computes on. So you can leave that 25 million row table in slow flash and pull only the few rows each token needs, about 450 bytes, while the small part that does the actual work stays in fast memory. The large model then costs almost nothing to run, because you never load most of it. It just sits in flash and gets sampled a little at a time. That idea is Google's Per-Layer Embeddings, from Gemma 3n and Gemma 4. Here it runs on the memory layout of a microcontroller instead of a phone or a GPU. As far as I can tell, nobody had tried it on a chip this small. SRAM (fast, tiny) the "thinking" core, used on every token PSRAM (medium) the output head and working memory FLASH (huge, slow) the 25M-param table, about 6 rows read per token (~450 B) The model was trained on TinyStories, so it writes short, simple stories and mostly keeps them coherent. It will not answer questions, follow instructions, write code, or know facts. That limit comes from the small part of the model that does the reasoning, and the memory trick does not change it. What is interesting here is the architecture, fitting a large model onto a tiny chip, rather than what a 28.9 million parameter model can say. The firmware, the wiring, and the flashing steps live in firmware/esp32_llm/README.md . The training, ablation, and quantization code is in src/ and experiments/ . The full method, the ablations, and the on-chip measurements are written up in RESULTS.md . TinyStories is the dataset this trains on: short synthetic stories simple enough that a small model can still learn to write coherently (Ronen Eldan and Yuanzhi Li, Microsoft Research, arXiv:2305.07759). The other half is Per-Layer Embeddings, Google's design from the Gemma models, which is what lets a big model fit on a small chip. Andrej Karpathy's llama2.c is why a lot of people, me included, believe you can train a tiny language model and run it in plain C at all. This grew out of that. I left the messy history in the repo on purpose. That includes a bug I found in my own parameter accounting, which had inflated an early number, and the corrected result that followed once I fixed it. The commit history and RESULTS.md show where the numbers moved and why.