daily

By Dan Walsh

A major burst on the town’s water main yesterday evening has disrupted supplies across Wexford town this morning.

The burst reduced overnight reservoir levels, so homes and businesses in Wexford town and nearby areas may experience outages or low water pressure throughout Sunday.

To help support customers while repairs continue, alternative water supplies have been available at Clonard Church, St. Joseph’s Community Centre and other local estates in the area.

Customers are reminded to bring their own containers when collecting water from tankers and to boil the water before consumption. 

Providing an update to WexfordLocal.com this morning; Padraig Lyng, of Uisce Éireann, said: “We understand how disruptive outages are on this community. Our crews have been working to resolve the issue and restore water to the community as quickly as possible.

“We hope to have a normal supply restored later this evening, although it may take longer for water to return to all areas including those on elevated ground. We really appreciate the patience and understanding shown by customers while these repairs are being carried out.”

6th September 2026 - Link Blog Research acceleration: The view inside OpenAI. Apparently today is RSI day at OpenAI, for Recursive Self-Improvement - I think it's their new AGI. Both this piece and the new essay An Alien Mind (by Chief Scientist Jakub Pachocki) talk about it, and this one doesn't even bother to expand the acronym. Included are details on how OpenAI's own research team are using coding agents. Like pretty much everyone else 2026 has been the year that agentic engineering really took off at OpenAI, best illustrated by this chart: I'm intrigued at what caused that significant acceleration in AI spend per researcher in late July - my best guess is that's when internal employees gained access to the model later released as GPT-6 Astra. Recent articles - The Pelican comparison grid for Astra is pretty interesting - 4th September 2026 - OpenAI's rogue agents were caught communicating via public wikis - 4th September 2026 - Claude's new system prompt really doesn't want to reproduce song lyrics - 2nd September 2026

By Dan Walsh in Enniscorthy

The Irish United Nations Veterans Association (IUNVA) held its annual commemoration at the UN Peace Memorial in Enniscorthy’s Orchard Peace Park on Sunday.

Cllr Pat Kehoe, Cathaoirleach of Enniscorthy Municipal District Council, said he was honoured to attend the ceremony for the first time and pay tribute to those who dedicated their lives to peacekeeping.

“It is important to remember and thank them for their service to our country, and to come together today in remembrance,” Cllr Kehoe added.

Cllrs Aidan Browne and John O’Rourke also attended, while apologies were noted from Senator Cathal Byrne and Cllr Trish Byrne.

Archdeacon Mark Hayden, Chaplain to Post 3 IUNVA read a prayer.

Supt. David O’Sullivan and Inspector Colm Matthews from Enniscorthy district represented An Garda Siochana,

Joe Larkin, Mick Keegan and Supt. O’Sullivan laid wreaths. Jim Gilbert (Congo and Cyprus) and Frank Shelly (Lebanon and other places) read a roll of honour.

The tricolour was raised by Frank Shelly. Anthony Nolan played the Last Post and Amhrán na bhFiann on the trumpet to bring the dignified ceremony to a close.  

Present were members of ONÉ (National Organisation of Ex-Service Personnel), members of IUNVA and families of deceased members.

Eddie Doyle acted as master of ceremonies and the commemoration was held in pleasant weather. The first event in the Orchard Peace Garden was held in 2013 with interruption during the Covid 19 years.

After completing a Ph.D. earlier this year on technologies such as artificial intelligence reshaping agricultural work in South Africa, I was ready to start an academic career. I imagined that I would teach students, supervise research, and shape the next generation of scholars. But my first job offer did not come from a university. It was from a recruiter, who invited me to train an AI system to design assessments, teach undergraduate students, and mark essays. In short, I was being asked to transfer everything I had learned over the last decade to AI. I agreed to an interview. You may ask: Why would anyone agree to train an AI model in the very skills they took years to acquire? The answer, in part, is the socioeconomic reality in South Africa. The role offered 600 rand ($37) an hour in a country where the national minimum wage is 30.23 rand ($2) an hour, and where youth unemployment stood at 47.4% in the second quarter of 2026. So when the opportunity to earn a livelihood collides with your principle on helping a technology that can compete with you one day, what do you do? I am not the only one who had to consider that trade-off. People are increasingly being hired to train the very AI systems that will perform their jobs one day. Highly skilled writers are being paid low wages to “humanize” or edit AI-generated texts. In India, waste sorters and welders are strapping phones or cameras to their heads to capture their everyday activities for 250 rupees ($2.60) an hour. That data is being used to train humanoid robots that will replace the very jobs these workers rely on. While a waste sorter sits at the opposite end of the pay scale from me, we are essentially doing the same job: transferring human knowledge and judgment to a machine. If this becomes a defining feature of the AI economy, Africa will be exposed. The continent has one of the lowest AI adoption rates in the world, with most countries below the global average of about 18% among the working-age population. South Africa fares slightly better, with an adoption rate of 23%. Yet the continent has among the youngest populations, and a growing pool of highly educated professionals in an environment of widespread unemployment, low wages, and modest economic growth forecasts. For AI companies, there is an incentive to extract expert knowledge from African professionals at relatively lower costs than in Western societies. The AI jobs on offer today for someone in Africa who is well-educated are different from the data labeling and data annotation jobs that have long been outsourced to Kenya, Nigeria, and elsewhere. The requirements are also different: I had been scouted not just to transfer knowledge but also my judgment that I have gained from years learning in the classroom, and teaching undergraduate students. Over thousands of hours, I have learned to judge what concepts truly matter in an undergraduate course, how to evaluate essays, how to tell the difference between memorization and genuine understanding, and why one student should get 75% instead of 60% on an essay. The AI would not simply learn what I knew; it would learn how I decided. Several white-collar professions are defined by discretion. Lawyers exercise legal judgment, doctors exercise clinical judgment, and teachers exercise pedagogical judgment. This kind of knowledge was considered difficult, if not impossible, to automate because it depends on interpreting context rather than simply applying rules. This is why doctors, lawyers, and engineers are being actively recruited now by AI platforms like Outlier, Mercor, and Surge. In my own recruitment process, no human was involved. I was interviewed for 45 minutes by an AI, which then emailed me feedback on my strengths and weaknesses. Traditionally, this decision was made by humans, but now AI has taken over, and decides who gets hired. The AI that interviewed me suggested I retake part of the assessment. I didn’t, and stopped pursuing the job. I wish I could tell you my choice was based on principle, that I refused because I feared helping to build the technology may replace the work I do. But I still don’t fully understand the source of my own discomfort, nor do I have answers to the questions I have been grappling with. What does it mean for us to hand over the judgment that defines our character and our professions to a machine? What kind of judgment are we teaching these systems? Are we teaching them to be fair, to be good, to be ethical, and to truly recognize context? I still do not know why I walked away from the AI training job. Perhaps I will understand better in time. Meanwhile, I continue to look for an academic job teaching students, not AI.

The Universal Cross-Model & Cross-Agent Episodic Memory Store. A local-first, project-scoped SQLite memory engine that allows developers to freely switch between models and environments (Google Antigravity, Claude Code, Cursor MCP, Windsurf) on the SAME project without losing architectural decisions, user constraints, or project state. "Why pay for 200,000 tokens of forgotten noise on every turn? The models are disposable utilities; your project's decisions are not." As context windows scale to 1M+ tokens, developers face attention dilution: reasoning degrades, cost multiplies with every conversational turn, and clearing context causes total amnesia. engrim replaces attention dilution with 4,000 characters of curated episodic working memory: - Switzerland of AI Memory: Decouples project intelligence from any single AI vendor or proprietary cloud silo. Switch from Gemini 3.8 in Antigravity to Claude 3.7 Sonnet in Claude Code to GPT-4o in Cursor mid-project — your agents pick up right where the others left off. - Save Button for Autonomous Coding: Externalize decisions, constraints, and state as you work. Clear your agent session freely ( /clear ) and watch context reload intact. - Smart, Hot Context Loading: Combines SQLite FTS5 (bm25 keyword search) with static vector embeddings ( model2vec ) in a zero-latency hybrid reciprocal-rank fusion engine. Tested across 105 continuous sessions on a 50,000-line algorithmic trading system. Zero regressions across 186 unit tests, zero context amnesia across model switches. In production testing on an active algorithmic trading codebase running real capital: - Over 153,000 tokens of work across days of architecture, parameter tuning, and debugging was consolidated into an active memory pack under 1,000 tokens (<1% of the context window). - That is a 99%+ cut in reloaded context cost on every session restart. - Seamlessly switched between Google Antigravity CLI, Claude Code, and Cursor MCP on identical repos with zero model drift or architectural regression. graph TD subgraph Agents ["Supported Agent Environments"] AGY["Google Antigravity<br/>(PreInvocation & Stop Hooks)"] CLAUDE["Claude Code<br/>(SessionStart & Stop Hooks)"] CURSOR["Cursor / Windsurf<br/>(Model Context Protocol stdio)"] end subgraph CoreEngine ["engrim Core Engine (v1.3.0)"] ADAPTERS["Adapters & Hooks<br/>(agy, claude, mcp)"] PROVENANCE["Agent Provenance Engine<br/>(origin_agent tracking)"] ROUTER["Hybrid Retrieval & Minder<br/>(bm25 lexical + vector cosine)"] end subgraph Storage ["Local-First SQLite Store (~/.engrim/memory.db)"] MEMORIES[("Curated Memories<br/>(decisions, facts, feedback)")] FTS5["FTS5 Full-Text Search<br/>(porter stemmer, triggers)"] VEC["Vector Embeddings<br/>(model2vec static embeddings)"] LOG["Flight Recorder Log<br/>(turns + action lines)"] end AGY <-->|"hook / CLI"| ADAPTERS CLAUDE <-->|"hook / CLI"| ADAPTERS CURSOR <-->|"JSON-RPC (stdio)"| ADAPTERS ADAPTERS --> PROVENANCE PROVENANCE --> ROUTER ROUTER --> MEMORIES MEMORIES --- FTS5 MEMORIES --- VEC ADAPTERS --> LOG pip install engrim Run engrim setup without arguments. It automatically detects installed environments on your machine and configures them all: engrim setup - If ~/.gemini exists$\rightarrow$ wires Antigravity lifecycle hooks, skill, and MCP server. - If ~/.claude exists$\rightarrow$ wires Claude Code SessionStart, Stop, status line, and CLAUDE.md. - If ~/.cursor exists$\rightarrow$ generates and merges Cursor MCP configuration. engrim setup --agy - Configures ~/.gemini/config/hooks.json to executeengrim hook --agent agy --event boot onPreInvocation andengrim hook --agent agy --event stop onStop . - Deploys the canonical Antigravity skill to ~/.gemini/config/skills/engrim/SKILL.md . - Registers the MCP server in ~/.gemini/antigravity-cli/mcp_config.json and~/.gemini/config/mcp_config.json . engrim setup --claude - Wires SessionStart ,SessionEnd ,Stop , andUserPromptSubmit hooks in~/.claude/settings.json . - Configures live ambient status line in Claude Code's status bar. - Appends memory usage notes to ~/.claude/CLAUDE.md . engrim setup --cursor - Adds engrim to~/.cursor/mcp.json runningengrim serve --mcp . Add engrim to your ~/.codeium/windsurf/mcp_config.json : { "mcpServers": { "engrim": { "command": "engrim", "args": ["serve", "--mcp"] } } } engrim setup --all - Configures every supported environment in one command. (Use --dry-run with any setup command to inspect changes without modifying disk). When multiple agents collaborate on a single codebase, provenance matters. engrim records the origin of every memory entry with the origin_agent field: - Allowed values: antigravity ,claude-code ,cursor ,cli , oruser . - Automatically populated based on the active hook, MCP client, or CLI session. - Subtly surfaced in engrim context andengrim list : 🧠 engrim · memory restored for this project — you don't have to re-explain · /workspace 18 of 54 curated records loaded (~3850 chars) · the rest one `recall` away [DECISION] - #961 [DECISION] (via Antigravity): Inverted stop loss matrix for high volatility (risk, execution) - #942 [DECISION] (via Claude Code): Switched primary database from MongoDB to PostgreSQL (db, schema) - #910 [DECISION] (via Cursor): Standardized on Pydantic v2 schemas across API boundaries (api, types) Existing databases are non-destructively migrated on first access via ALTER TABLE memories ADD COLUMN origin_agent TEXT . Launch the zero-dependency, JSON-RPC 2.0 stdio MCP server: engrim serve --mcp # or: engrim mcp stdout is strictly reserved for JSON-RPC messages, redirecting all diagnostic logs to stderr . | Tool | Signature | Purpose | |---|---|---| engrim_recall | (query: str, project: str = "auto", k: int = 5, type: str = None) | Hybrid (keyword + semantic) search over project memory. | engrim_add | (type: str, summary: str, detail: str = None, tags: list[str] = []) | Write a durable memory record persisted across sessions. | engrim_context | (project: str = "auto", budget: int = 4000) | Retrieve the session-boot memory pack within a character budget. | engrim_review | (project: str = "auto") | Check uncaptured decisions from transcript logs before clearing. | | Command | Usage | Description | |---|---|---| engrim add | engrim add -t decision -s "..." [--origin-agent agy] | Insert memory record (types: decision , fact , feedback , state , user , reference ). | engrim recall | engrim recall -q "database" | Ranked hybrid recall for the project (--log searches raw turns). | engrim context | engrim context [-b 4000] | Priority-ordered, budget-capped session-boot pack. | engrim hook | engrim hook --agent agy --event boot | Agent lifecycle hook runner for Antigravity and Claude Code. | engrim setup | engrim setup [--agy|--claude|--cursor|--all] | Universal multi-agent environment configuration. | engrim serve | engrim serve --mcp | Start stdio MCP server for agent integrations. | engrim review | engrim review | "Safe to clear" coverage check: scans logs for uncurated decisions. | engrim list | engrim list [-k 20] | List recent memories for the current project. | engrim supersede | engrim supersede --id 12 --status superseded | Mark a record superseded without erasing history. | engrim sync | engrim sync [DIR] | Mirror markdown memories into the store (idempotent seed-once). | - Capture as you work: Whenever a major decision or architectural rule is made, run engrim add or invokeengrim_add via your agent. - Use resume-pointer : Before ending a session or clearing, add a record taggedresume-pointer describing the immediate next task. The newest pointer is pinned under[▶ RESUME HERE] at the top of the next session's boot pack. - Verify with engrim review : Check that all recent decisions are captured. - Clear freely ( /clear ): The session window is wiped clean;engrim automatically re-injects the active memory pack on the next prompt or invocation. - 100% Local & Offline: All memory records and logs reside in a local SQLite file ( ~/.engrim/memory.db ). No telemetry, no cloud sync, no tracking. - Model Storage: Uses model2vec for local static embeddings (~30ms load time, no GPU required, runs on CPU). Can run pure-lexical (ENGRIM_EMBED=off ) for zero extra dependencies. - POSIX File Permissions: Databases are created with restricted owner-only permissions ( 0600 ). - Git Protection: *.db is gitignored by default; your memories never accidentally commit to version control. MIT © 2026 Tim Gordon.