Church grounds and graveyards are becoming havens for birds, bees and bats in a groundbreaking biodiversity project. The initiative, which began in Clare in 2023, has now spread to more than 22 Catholic and two Church of Ireland churches in the county – in counties Louth, Wexford, Limerick, Cork, Leitrim and Knock Shrine in Mayo. The original pilot project, led by Clare County Council’s biodiversity officer Barry O’Loughlin, came about after the Irish Bishops’ Conference agreed that parishes would return 30 per cent of church grounds to nature by 2030. Driven by the council, which organised rewilding presentations and trained church groundkeepers, it has changed the appearance of church grounds usually managed as manicured land. I remember seeing a video of how a tightly cut lawn was the equivalent of a desert for creatures Six other biodiversity officers have now followed Clare’s lead, extending the initiative to a further 20 churches. “Church grounds are the focal point of every town and village in the country and a great way to provide a refuge for wildlife and give biodiversity visibility in the community,” said O’Loughlin. Wildflowers and pollinator-friendly trees have been planted and rare orchids have emerged. Swift and owl nest boxes have been installed on church buildings, bat roosts were erected on trees and bird tables and feeders were also installed. The project can also take credit for the arrival in Clare of a barn owl chick, born in one of the nesting boxes installed on a farm by the council. The initiative is being showcased as part of #YourCouncilDay on Friday June 26, the national campaign to generate awareness of services and work carried out by county and city councils. The Bishop of Killaloe, Fintan Monahan, gave his blessing to the project by establishing a wildflower meadow on the grounds of his residence, Westbourne in Ennis. “I remember seeing a video of how a tightly cut lawn was the equivalent of a desert for creatures and that changed my mindset about what we regard as neat and tidy,” he said. “There are bees and an increase in bird life; I’ve seen swallows and squirrels around the trees.” Bishop Monahan said he was surprised by the success of the initiative, which was sparked by an encyclical from Pope Francis in 2015. In Laudato Si’, the first in the history of the Catholic Church to be devoted to the environment, the Pontiff called on individuals, communities and nations to work together to preserve the rich diversity of life on earth. Biodiversity officer with Mayo County Council Sheila Murphy said that with visitor numbers close to one million annually, Knock Shrine was also an ideal site to showcase actions for biodiversity.

This experiment shows an AI agent autonomously improving a small GPT's training recipe. Using AutoResearch (Karpathy et al.) – which iteratively edits training code, runs experiments, and keeps only changes that lower validation bits-per-byte (BPB) – the agent ran 123 experiments over ~14 hours on a single H100 GPU. Each line traces a system's best BPB as experiments accumulate: Fugu-Ultra is in bold red (solid = mean over three seeds, dashed = best single run), with three frontier-model baselines (Model A, B, and C) faded behind it, and the callouts mark each new improvement the agent found on its own — spanning batch size, model depth, learning rates, and optimizer settings. Fugu-Ultra finishes with the best mean BPB (0.9774 ± 0.0019), ahead of Model C (0.9781), Model B (0.9793), and Model A (0.9822), and its best single run reaches 0.9748, leading every baseline. This suggests that orchestrating multiple strong models can outperform any individual frontier model on agentic ML research. 例1 — AutoResearch / LLM学習 AIエージェントに小規模なGPTの学習レシピを自律的に改善させる実験。学習コードを反復的に書き換え、実験を実行し、検証用 bits-per-byte（BPB）を下げた変更だけを残していくエージェント型フレームワーク AutoResearch（Karpathy et al.）を用い、エージェントは単一のH100 GPU上でおよそ14時間にわたり123回の実験を実施した。各線は、実験が積み重なるにつれて各システムが達成した最良のBPBの推移を表している。Fugu-Ultra は太い赤の線（実線＝3シードの平均、破線＝最良の単一実行）で示し、その背後に3つのフロンティアモデルのベースライン（Model A・B・C）を淡色で重ねている。吹き出しは、エージェントが自ら見つけた改善点をそれぞれ示しており、バッチサイズ、モデルの深さ、学習率、オプティマイザの設定など多岐にわたる。Fugu-Ultra は最終的に最良の平均BPB（0.9774 ± 0.0019）を達成し、Model C（0.9781）、Model B（0.9793）、Model A（0.9822）を上回った。最良の単一実行では 0.9748 に到達し、すべてのベースラインを上回っている。これらの結果は、複数の強力なモデルをオーケストレーションすることで、エージェント型のML研究において単体のフロンティアモデルを上回り得ることを示唆している。 This case study tests whether the reading order of classical Japanese kana letters (仮名消息) can be recovered — letters whose scattered chirashigaki ("scattered-writing") layout makes that genuinely hard even for trained readers of classical Japanese. Each model is given the character bounding boxes together with a rough set of reading-order rules, and writes code that outputs the order the characters should be read in; here it runs on a letter written in 1610 by Hōshun'in (芳春院, 1547–1617), scored by NED (a score based on normalized edit distance from an expert's ground-truth order, where 1.0 is a perfect match). Several frontier models were put through the identical pipeline, but none came close to Fugu-Ultra on this letter: Model A reached only NED 0.24 and Model B scored no better, both far below Fugu-Ultra's 0.80, while Model C produced no predictor at all. The clip shows the two extremes — each panel draws its predicted path in red over the expert's ground truth in green: Fugu-Ultra (top) traces the letter almost exactly, while Model A (bottom) jumps all over the page. (Letter held by the Keio Institute of Oriental Classics.) 例2 — 仮名消息の読み順推定 本ケーススタディは、仮名消息（古典日本語のかな書状）という歴史的資料における読み順の推定問題を対象とする。仮名消息は、文字を紙面に散らして記す「散らし書き」という形式で書かれているため、古文書を読み慣れた人でも文字の読み順を正しく判定することは難しい。そこで各モデルに対して、文字を囲む四角形（バウンディングボックス）と読み順の大まかなルールを与え、文字の読み順を推定するコードを出力させた。実験の対象には1610年に芳春院（ほうしゅんいん、1547–1617）が記した書状を選び、NED（専門家による正しい読み順との正規化編集距離にもとづくスコア。1.0が完全一致）で評価した。複数のフロンティアモデル（A-C）を同一のパイプラインに通したところ、Fugu-Ultraの結果は他のモデルを大きく引き離した。Model AはNED 0.24、Model Bもそれと大差なく、いずれもFugu-Ultraの0.80には遠く及ばない。さらにModel Cはまともなコードを一回も出力できなかった。モデルによる読み順の違いを可視化するために、専門家による正解の読み順（緑）の上に、推定した経路（赤）を描いて映像化した。Fugu-Ultra（上）が読み順をほぼ正確になぞる一方、Model A（下）は紙面全体をあちこち飛び回り、両者は大きく異なる結果を示している。 図：芳春院消息（慶應義塾大学斯道文庫蔵） In this benchmark, each of Fugu-Ultra and 3 frontier models is given a single prompt to write a Rubik's Cube solver from scratch in pure Python — no off-the-shelf solving libraries allowed — and the resulting program is run locally on a held-out set of 300 randomly scrambled cubes. Solution quality is measured by the number of moves a solution uses, where lower is better. Fugu-Ultra and the frontier Model A wrote solvers that ran and solved all 300 cubes, while Model B and Model C each shipped sophisticated-looking code that crashed on execution and returned no valid solution at all (0/300). The clip follows cube #17: from the same scramble, Fugu-Ultra's solver reaches the solved state in 19 moves while Model A needs 21 — and across all 300 cubes Fugu-Ultra averages 19.72 moves versus 19.76 for Model A, both right at the optimal frontier, with Fugu-Ultra never a move longer than Model A on any cube (7 wins, 293 ties, 0 losses). 例3 — ルービックキューブ・ソルバー 本ベンチマークでは、Fugu-Ultraと3つのフロンティアモデルそれぞれに、純粋なPythonのみでルービックキューブソルバーをゼロから実装するよう単一のプロンプトを与えた。既存のソルバーライブラリの使用は禁止とし、生成されたプログラムをランダムにスクランブルされた300個のキューブからなるホールドアウトセットに対してローカルで実行した。解法の質は手数で評価し、少ないほど良いとする。Fugu-Ultraとフロンティアの Model A は300個すべてのキューブを解くソルバーを生成したが、Model B と Model C は一見洗練されたコードを出力したものの、実行時にクラッシュし、有効な解を一つも返せなかった（0/300）。映像はキューブ#17の様子である。同一のスクランブルに対し、Fugu-Ultraのソルバーは19手で完成状態に到達したのに対し、Model A は21手を要した。300個全体の平均では、Fugu-Ultraが19.72手、Model A が19.76手と、いずれも最適解の水準にあり、Fugu-Ultraが Model A より手数が多かったケースは一度もなかった（7勝・293引き分け・0敗）。 Task: Create a mechanical iris in CAD, like a camera aperture, where multiple blades move together to open and close the central hole. For each model, we show both the generated detailed CAD itself and a simplified view that makes the structure easier to see. In the CAD generated by Fugu Ultra, the blades rotate around outer pins and clearly open and close the aperture. In contrast, the CAD generated by the other models shows problems such as gaps appearing, weak linkages, or the aperture not closing fully. 例4 — CAD メカニカルアイリス タスク：カメラの絞り（アパーチャ）のような、複数の羽根が連動して動き中央の穴を開閉する機械式アイリスをCADで作成する。各モデルについて、生成された詳細CAD（Detailed CAD）そのものと、構造を見やすくするための簡易ビュー（Simplified view）の両方を示す。Fugu Ultraが生成したCADでは、羽根が外側のピンを軸に回転し、アパーチャを明確に開閉できている。一方、他のモデルが生成したCADでは、隙間ができてしまう、リンク機構が弱い、アパーチャを十分に閉じきれていない、といった問題が見られる。 Four blindfold chess games, back to back. Every model plays the same way — no board shown — holding the full game in memory. Fugu outplays four strong opponents: three leading frontier models and a 2100-Elo Stockfish engine, staying accurate where they drift and ending each game in checkmate. 例5 — 目隠しチェス 4局の目隠しチェスを連続して対局している。すべてのモデルは同じ条件でプレイし、盤面は一切表示されず、ゲーム全体を記憶の中に保持しながら指し手を進める。Fugu は4つの強力な相手——3つの主要なフロンティアモデルと、2100-Elo の Stockfish エンジン——を打ち負かした。相手が手を乱していく場面でも正確さを保ち、いずれの対局もチェックメイトで終えた。 This benchmark uses a single anonymized equity over one historical 50-week window and is intended to compare sequential, no-look-ahead decision-making rather than to establish generalizable trading performance. Past performance does not guarantee future results, and results may not transfer to other assets, time periods, or live markets. Each model makes online trading decisions on anonymized STOCK_X, using only current and past weekly market data: opening, high, low, and closing prices, volume, returns, moving averages, volatility, drawdown, portfolio state, and prior feedback. Starting with $10,000, the agent chooses whether to buy, hold, or sell, and what fraction of cash or shares to trade. After each action, the next week's price is revealed and the portfolio is updated, so the model must adapt from feedback rather than seeing the future. Across five runs of the identical 50-week pipeline, Fugu-Ultra grew the portfolio to $11,943.22 ± $633.86, a +19.43% mean return, while the other frontier models reached their return less than +15%. 例6 — 株式トレーディング 匿名化された単一銘柄を1つの過去50週間のウィンドウで用いるこの株式トレーディングのベンチマーク。汎用的なトレーディング性能を立証するためではなく、先読みのない逐次的な意思決定を比較することを目的としている。過去の実績は将来の結果を保証するものではなく、結果が他の資産・期間・実際の市場に当てはまるとは限らない。各モデルは、匿名化された STOCK_X に対して、現在および過去の週次マーケットデータ——始値、高値、安値、終値、出来高、リターン、移動平均、ボラティリティ、ドローダウン、ポートフォリオの状態、直前のフィードバック——のみを用いてオンラインでトレーディングの意思決定を行う。1万ドルからスタートし、エージェントは買い・保有・売りのいずれかと、現金または株式のどの割合を取引するかを選択する。各アクションの後に翌週の価格が開示され、ポートフォリオが更新されるため、モデルは未来を見るのではなくフィードバックから適応しなければならない。同一の50週間パイプラインを5回実行した結果、Fugu-Ultra はポートフォリオを 11,943.22 ± 633.86 ドルまで成長させ、平均リターンは +19.43% に達した。一方、他のフロンティアモデルのリターンはいずれも +15% 未満にとどまった。

Legislation to allow the Irish Naval Service to board ships linked to Russia’s “shadow fleet” will be enacted in time for the start of Ireland’s EU presidency in 10 days, the Minister for Defence has said. Amendments to the Defence Act were announced in March amid growing concerns about the activities of ships suspected of being part of Russia’s “shadow fleet” and reports of damage to subsea infrastructure in European waters. Strategically important undersea communications cables lie off the Irish coast, along with gas pipelines and electricity interconnectors. Currently, the Defence Forces have the authority to enforce certain laws at sea on behalf of other government agencies. But this legislation will, for the first time, give them specific maritime enforcement powers under the Defence Act. On Sunday, Helen McEntee appeared on RTÉ Radio 1’s This Week programme and was quizzed on whether the legislation would be ready for the July 1st deadline she set earlier this year. She said it has passed all stages in the Dáil and is now in the Seanad, adding: “It is absolutely the ambition that this will be done by July 1st, so we’re going through the different stages, and yes, this will be done by July 1.” The new powers are necessary, she said, so Ireland’s Defence Forces can respond to “new and emerging threats”. The “shadow fleets” are made up of “large illegal tankers from Russia that are quite literally traversing our waters”, she said. “Not only are they selling oil that is funding and fuelling the war in Ukraine, but they are also an environmental hazard. “But also, there are serious questions as to what it is that they are doing in our waters.” She said she wants defence force personnel to have specific legislation so “they can board, that they can direct, and that they can remove them from our economic waters”.

By Dan Walsh on Vinegar Hill

The Longest Day, Father’s Day and 228^th anniversary of the Battle of Vinegar Hill were celebrated in ceremonial style on Vinegar Hill, overlooking Enniscorthy town, on Sunday evening.

The principal speaker was historian Jacqui Hynes, joint editor of Wexford Rebels of 1798 (with Ronan O’Flaherty) who said; “Archaeological research reveals this site to be potential mass graves on this battlefield, silent testimony to the scale, the tragedy and the number of families torn apart, marked and unmarked graves, either on this hill and the roads from here contains the remains of many who died violently, including non-combatants reminding us that the cost of the rebellion was borne by entire households not just the men who fought.

“We can only surmise that for many survivors that stood on this hill 128 years ago today the impact was lifelong. Some returned home to shattered communities, others lived with the grief of lost family members.

“These men were not faceless. They were fathers, sons and neighbours whose families carried their loss for generations. As we celebrate the fathers in our life today (Father’s Day) let us remember all the men, women and children who stood on Vinegar Hill their hearts filled with courage and fear.

“May we honour them by cherishing the peace they never knew and by remembering this battlefield site with the respect and the dignity it deserves,” concluded Ms. Hynes.

Cllr Barbara-Anne Murphy, Cathaoirleach of Enniscorthy Municipal District Council and Chair of the 1798 and Enniscorthy Castle Board said; “we are here on the 228^th anniversary of the battle of Vinegar Hill to remember all those who died here in 1798.”

Very Rev. James Cullen, St. Aidan’s Parish and the Reverend Canon Nicola Halford, Rector Enniscorthy Monart Union of Parishes shared in ecumenical prayers.

Wreaths were laid at the ’98 memorial by Cllr Barbara-Anne Murphy and Ms. Emily Murphy. 

Maura Bell, Manager of the National 1798 Rebellion Centre acted as master of ceremonies. She welcomed Cllrs Barbara-Anne Murphy, Aidan Browne, Trish Byrne, John O’Rourke and Pat Kehoe. There was an apology read from Senator Cathal Byrne who could not attend.

There was a welcome for participants including ’98 Le Cheile, Ballindaggin Pipe Band, Enniscorthy Historical Re-enactment Society.

The event commenced with a march by the members of St. Colman’s Pipe Band, Ballindaggin, up Vinegar Hill Lane and concluded with Amhrán Na bFhiann played by the band.   

Apertus Mini A set of 16 small language models released to demonstrate distillation and quantization techniques. Developed by the Swiss AI Initiative as a collaborative effort between EPFL, ETH Zurich, and CSCS. Open weights, open data, open science. Training data, code, weights, methods, and alignment principles — all documented and reproducible. Apertus is to AI as Open is to Source. Built to meet EU AI Act requirements: the model respects opt-outs, removes PII, prevents memorization. A global foundation to build on. Competitive with top open models at an equivalent scale of 8B and 70B parameters. Multilingual from day one — trained on 1000+ languages. Swisscom is a Strategic Partner of the Swiss AI Initiative.

Andrew Marble marble.onl andrew@willows.ai June 21, 2026 There was a time not too long ago when using Linux entailed some professional risk1. First there was compatibility: you may not have been able to render a Word document or PowerPoint correctly, and you might have had to trust Open Office’s export capability to render docs the way you wanted. There might have been specialty file formats you couldn’t easily view and so couldn’t collaborate. And second, the software ecosystem was just worse generally. There were lots of half-build open-source projects trying to achieve the functionality of mainstream software, but they always had rough edges. I, embarrassingly, stayed on Windows until I left academia over Matlab. Nowadays I think this issue has largely disappeared. Most productivity software has a web-app, Linux is more mature, open-source software is better. I’m sure that there are all sorts of application specific software (CAD?) that still require a Windows machine, but the gap is much narrower and Linux + open source generally aren’t the “sacrifice” they once were generally. There remains a clear penalty for being an open2 LLM user. Every leaderboard consistently gets topped by proprietary models served over API. Today on June 21, 2026, Claude and GPT are at the top of the Artificial Analysis intelligence leaderboard. That’s from the performance side. The compatibility side is worse too. Claude code just works, and more generally, the big two provide nice APIs that make them easy to use, and, even if it’s a low bar, are “trustworthy” in the sense that we’ve largely all agreed we don’t mind sending them our LLM queries and trust them to handle them appropriately. Open models are served via various means, some by the companies that released them and some by third parties like OpenRouter. Unfortunately, both of these routes are dodgier in terms of privacy and data sharing, and I would not feel the same comfort sending API calls containing client or confidential data to them3. The other option or course is to run them yourself. This solves the privacy issue but is at least two of expensive, complicated, and comparatively slow. Up until recently, open models had mostly been a hobby for me. I’ve tinkered with them since the original Llama leak, and occasionally used them when I has a niche use case, but for most professional work, I stuck with the Big 2. This appears to be changing, with Claude’s ID verification rollout4. It was inevitable that things would get worse for users, and the writing was on the wall anyway recently with all the new “safeguards” on recent models and the whole Mythos thing. I’m not going to spend time talking about why I’m not going to indulge ID verification (or the LARPing that surrounds it) but what is immediately concerning is what kind of professional penalty it will incur to stop using the top models. I’m hoping it’s going to be minimal. I’m already set up to run a range of open models either locally or in the cloud, there are good coding harnesses for open models, and most importantly the open models are now very close to the leaders and typically trail only by a few months. This doesn’t feel like 2008 Linux vs Windows, it’s much closer. I expect productivity will take a short-term hit, but don’t think it’s a deal breaker the way switching from Matlab to GNU Octave would have been when I was doing research. I’m assuming a technical job that includes general purpose work that requires productivity software like MS Office etc.↩︎ I use “open” here to mean the weights are available, I have written extensively on why I don’t consider this automatically open source, but I’m using “open” as shorthand. And unlike when I addressed this previously, the current leading open models generally are MIT licensed which I do consider open source, though many don’t.↩︎ I won’t dwell on this, happy to be corrected, but in my experience under normal circumstances nobody balks if you tell them you’re using OpenAI or Anthropic. If you’re sending requests to Deepseek or OpenRouter etc. there are likely to be more concerns, regardless of the underlying truth.↩︎ https://support.claude.com/en/articles/14328960-identity-verification-on-claude↩︎

During a week that saw new trailers for Final Fantasy 7 Revelation, Kingdom Hearts 4, and Persona 6, I was surprised to see that one of the most talked-about reveals of Summer Game Fest was Final Fantasy Resonance. Just a few years ago, the prospect of Square Enix upcycling a lowly mobile game into HD-2D would have prompted howls of scandalized disapproval from many of the same weebs currently hopping on the hype train. But the buzz around Resonance does speak to a growing appetite for mid-sized experiences that feel plucked directly from the 16- and 32-bit eras — and The Adventures of Elliot: The Millennium Tales exemplifies that trend in the best possible way. Co-developed by Team Asano (a team within Square Enix) and Claytechworks, The Adventures of Elliot feels like playing a lost SNES or PS1-era classic for the first time. I can’t say that it does anything particularly revolutionary by the standards of 2026. But the pacing is tight, the boss fights are wonderfully varied, the puzzles are just crunchy enough, and the music and visuals bring to mind a fairy-tale storybook. While the game spans four distinct time periods, the compact map and 25-hour runtime invite exploration without ever feeling daunting. There’s a Ghibli-esque earnestness to the storytelling in Adventures of Elliot, which comes off a little hokey at first but won me over eventually. The story opens in the Kingdom of Huther, and Elliot has volunteered to retrieve a “valuable item” for an older gentleman. (Turns out, it’s just a lost comb from his deceased wife.) Elliot may be an adventurer by trade, but we soon learn he’s far more of a boy scout than a treasure-hunting rogue. He’s a kindly big-brother figure to the residents of the town orphanage, alternating tales of derring-do with cautious lessons about weapon handling. The tale kicks off in earnest when the King asks Elliot to investigate some newly discovered ruins, and then to thwart a villain who travels back in time to seize the forgotten power of magic. Our hero accepts the task without hesitation. “My family believes me to be the greatest adventurer in the world. I’d hate to disappoint them,” he says. It’s a moment that sets the tone for the game the whole way through. If you’re looking for a 2deep4u protagonist in the vein of Final Fantasy 7’s Cloud Strife, you’re not going to find that here. The Adventures of Elliot is an unambiguously chipper experience, where even seemingly calamitous plot twists can be overcome by chatting with cordial townsfolk or revisiting a familiar dungeon. The Adventures of Elliot is Team Asano’s first action RPG. (The team has mostly stuck to turn-based games, though it took a swing at the tactics subgenre with 2022’s Triangle Strategy.) Like the SNES-era Zelda games that partially inspired it, there’s a heavy emphasis on exploration, particularly using new skills and weapons to explore areas that had previously been out of reach. Elliot can break open conspicuously cracked walls with a well-placed bomb, whomp stakes into the ground with his hammer, and trigger distant switches with a carefully aimed arrow or boomerang. Elliot’s fairy sidekick, Faie, has several nifty tricks of her own. (You may have already heard that she yaps a lot, and mercifully there is an option in the settings menu to make her talk less.) These abilities include lighting fires to illuminate dark spaces, warping Elliot over vast gaps, and creating an illusory double of Elliot, which can be used to solve puzzles as well as in combat. Since the story spans four time periods, you’ll be revisiting the same locations a lot, and they’re all populated by the same handful of enemies. The overworld map is largely identical between time periods, and dungeons have only minor variations. And all of those dungeons are quite short, since you'll visit them more than once. This works out far better in practice than it sounds on paper, thanks to a generous fast-travel system both within and outside dungeons that makes the repetition a lot less irritating than it would be in less considerate hands. For instance, if a dungeon has a story-essential item in one time period, returning in another era might yield a nice optional item, like a weapon upgrade or Shard of Life (i.e., a legally distinct Zelda heart container fragment). In the other two periods, walls will have collapsed, preventing you from venturing deeper. If there’s nothing to find in a given location, Faie will let you know as soon as you enter or once you’ve reached a dead end, so you don’t whip yourself into a lather searching for a puzzle solution or key that doesn’t exist. The few times I felt annoyed by having to retrace my steps — or “rediscover” a place I’d already visited in every other time period — it rarely took more than 10 or 15 minutes. Given that The Adventures of Elliot invites Chrono Trigger comparisons with its era-hopping, I was hoping that would open up some interesting possibilities where exploration was concerned. If you drain a flooded dungeon in the past, will new areas open up in the future? Well, no. There’s not really any of that. Your actions in the past don’t have a tangible impact on future exploration. It feels like a major missed opportunity, especially when the maps are so similar from era to era. Time travel really only serves a storytelling purpose in The Adventures of Elliot, and it does so very simply. Our hero eagerly blabs about his era-hopping to literally everyone he meets, including numerous people who turn out to be consequential historical figures. No one seems to think him meddling with the past is a problem, and it never actually is. I can see why it may not be a great fit with the story's sunny vibes, but I would have appreciated more attention to the unintended consequences of Elliot altering the past — even if it was handled in a lighthearted way. Secret of Mana and The Legend of Zelda: A Link to the Past offer more flattering points of comparison. Like those games, The Adventures of Elliot doesn’t have an experience-based progression system, which means random encounters are mostly breezy and even entirely skippable later on. On the other hand, you’re encouraged to mix up your strategies and think creatively when it comes to boss fights. Sometimes you’ll need to deflect attacks back toward your opponent with your shield. Other enemies might be vulnerable to bombs, but you’ll have to be careful about your timing. Thankfully, the game never locks you into one way of doing things. It helps that your weapons aren’t the only tools at your disposal in a challenging fight. One boss encounter pits Elliot against a snail in an icy shell. Entirely by accident (I had meant to use the dash ability), I realized that Faie’s torch-lighting skill could be used for small but consistent chip damage against the ice-elemental baddie. When I struggled to time my bomb tosses to crack its shell, I used Faie’s duplication skill, allowing the boss to sidestep (side-slime?) my projectile and bumble straight into the path of my doppelganger’s explosive. When other bosses started throwing massive area-of-effect attacks my way, I adapted by leaning on Faie’s warp skill to quickly get behind the enemy and whale on its unprotected rear. Rather than just spamming my strongest spells or attacks, I came away from these battles buzzing with kooky new ideas I wanted to try. The magicite system also opens up a ton of combat customization options, allowing you to lean into the strengths of your favorite weapons and find new uses for your backup options. You can use magicite shards to create randomly generated accessories for each weapon, and to increase how many of those accessories you can equip. I most appreciated the magicite that offered new riffs on familiar abilities, like increasing the number of boomerangs in each throw, making bombs take longer to detonate, or adding elemental attributes to my hammer and sickle. Each weapon has 15 types of magicite that mostly boost stats, like increased critical rate, higher base damage, or decreased charge time. But by mid-game, I’d pretty much tinkered with all of them and settled on a consistent loadout. After that point, adding more magicite capacity felt nice to have, but not essential. While a couple late-game side quests overstay their welcome, a big part of this game’s appeal is that it can easily be enjoyed in shorter sessions. You can’t really drop in on Tears of the Kingdom or Persona 5 for 30 minutes and make meaningful progress, but you absolutely can in The Adventures of Elliot. Though the scope of the story is compact, I was pleasantly surprised by the way the stakes ratcheted up over time to reach a thrilling conclusion. While I do wish the time-travel aspects had more depth, if you have fond memories of Secret of Mana and/or A Link to the Past — or are simply craving a satisfying RPG experience that won’t take over your life — you’ll have a fantastic time with The Adventures of Elliot. It’s an easy game to pick up and a hard one to put down. The Adventures of Elliot comes to Nintendo Switch 2, PlayStation 5, Windows PC, and Xbox Series X on June 18. The game was reviewed on Switch 2 using a prerelease download code provided by Square Enix. You can find additional information about Polygon’s ethics policy here. JRPG fans are eating well this year From Final Fantasy 7 Revelation to Kingdom Hearts 4, JRPG fans are about to have a feast

Fiona Fung leads the teams behind Claude Code and Cowork at Anthropic (overseeing Boris Cherny and the entire engineering and PM team). Before Anthropic, she spent 11 years at Microsoft building Visual Studio and TypeScript and then moved to Meta, where she started Facebook Marketplace (now generating over $100 billion in GMV annually), worked on Meta’s first smart glasses and AR glasses, and led infrastructure, growth, integrity, and safety teams at Instagram. She’s been an engineer for over 25 years and has a unique perspective on how the role of building software is changing.

In our in-depth conversation, we discuss:

1. What she’s learned about running a team that’s shipping 8x more code than before

2. Which roles AI will transform next

3. Specific ways her team uses AI

4. How Claude “routines” have changed how she operates as a manager

5. The context-switching problem no one has solved yet

6. The biggest unsolved problem in AI

7. What keeps her up at night

Where to find Fiona Fung:

• LinkedIn: linkedin.com/in/fionafung

Referenced:

• Running an AI-native engineering org: https://www.youtube.com/watch?v=igO8iyca2_g

• Head of Claude Code: What happens after coding is solved | Boris Cherny: https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens

• Today, Anthropic engineers on average ship 8x as much code per quarter as they did compared to 2021-2025: https://x.com/AnthropicAI/status/2062568864240836995

• Visual Studio: https://visualstudio.microsoft.com

• Joseph Campbell’s quote: https://www.goodreads.com/quotes/192665-the-cave-you-fear-to-enter-holds-the-treasure-you

• Life-changing Cowork use case: https://x.com/lennysan/status/2059664455001334124

• Introducing Claude for Small Business: https://www.anthropic.com/news/claude-for-small-business

• Conversations with Tyler podcast: https://conversationswithtyler.com

• Sheryl Sandberg on Facebook: https://www.facebook.com/sheryl#

• Amélie on Prime Video: https://www.amazon.com/Amelie-Jean-Pierre-Jeunet/dp/B0DQ4S3N45

• Spirited Away on HBO Max: https://www.hbomax.com/movies/spirited-away/3deab668-d0a4-4a8d-9bc8-0952a0ad836e

• Nausicaä of the Valley of the Wind on HBO Max: https://www.hbomax.com/movies/nausicaa-of-the-valley-of-the-wind/ed66031b-6353-4019-ba54-35488468a4db

• Sweet Sisters Bodycare: https://sweetsistersbodycare.com

• Anthropic events: https://www.anthropic.com/events

• Clare Pooley’s quote: https://www.goodreads.com/quotes/11305360-in-a-world-where-you-can-be-anything-be-kind

Recommended books:

• Margaret Atwood’s books: https://www.amazon.com/stores/author/B000AQTHI0?ccs_id=0027a474-cd59-4a3a-bcd7-9b173c27d530

• Haruki Murakami’s books: https://www.amazon.com/stores/Haruki-Murakami/author/B000AP7AFI

• The Little Prince: https://www.amazon.com/Little-Prince-Antoine-Saint-Exup%C3%A9ry/dp/0156012197

• Nausicaä of the Valley of the Wind: https://www.amazon.com/Nausica%C3%A4-Valley-Wind-Box-Set/dp/1421550644

• High Output Management: https://www.amazon.com/High-Output-Management-Andrew-Grove/dp/0679762884

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

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

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If you go to your favorite social media site, you will find it full of posts that start to look suspiciously similar to each other:

Many of the comments to these posts are also generated by AI. So are an increasing number of academic papers and New York Times opinion articles, and, apparently, award-winning short stories. If you use AI a lot, you probably have noticed how much AI writing is around you (frequent AI users have historically done quite well identifying AI writing), if not, I promise you it is much more than you think.

It isn’t just the sameness of the AI writing, though that eventually gets to be tedious enough that I find myself skipping writing on even interesting topics if my internal “AI detector” goes off. It is also that badly prompted AI writing produces very little meaning per word, taking you in intellectual circles instead. We are trained to read well-crafted sentences and intellectual sounding texts as the result of effortful human work and thus pay attention to these AI written comments when we see them. But there is often no human meaning there, these posts are just meaning-shaped attention vampires that take mental effort to decode and give you no equivalent understanding in return1.

But using AI for writing has a cost beyond turning off readers, it risks undermining the development of an important human task. I am lucky enough to have been writing for decades, and I have developed my own style which I think shines through whether I am writing a book, a tweet, or a blog post. That style took a lot of super annoying work to get to: good teachers and rewrites and mean online comments all contributed. If the AI does fine writing, I could skip all of that, but I would have done so the cost of giving up something that has turned out to be very important to my career and my happiness.

This is not a condemnation of using AI to help with writing in any way. I think AI can be a fantastic tool for good writers (I have AI check all of my writing and roleplay different reader perspectives to see if I missed something important). For those who struggle with communication, AI can help get their ideas across better, and writing may not be thinking for everyone. Plus, a little bit of effort can make AI writing less cliche, more personal, and more worth using (in moderation). So, this is instead a condemnation of using AI as a default, or, even worse, without thinking at all. Balancing using AI with our own mental abilities is going to be a defining challenge of the coming years.

Subtle changes, big outcome differences

The clearest place to see this is in education, where two papers with an overlapping research team (including peers at Wharton) do a good job illustrating the difference between using AI to shortcut thinking and to help thinking. The first paper was an experiment at a high school in Turkey with about a thousand students learning math. One group used plain ChatGPT, the other had no AI access. The students with ChatGPT did their homework better and thought they were learning more, but at test time, they underperformed their classmates without ChatGPT. That is because the AI, designed to be a helpful assistant, was really just giving them answers, and actual learning requires mental effort. By short-circuiting effort, you short-circuit learning. That is why the initial results of AI on learning in classrooms can be so worrying.

Yet we can see a different result in a second paper from many of the same authors when they ran a five-month Python course across ten high schools in Taipei with close to a thousand students. Students who were given a personalized sequence of problems by an AI tutor scored 0.15 standard deviations higher on a final exam taken without AI help. By some estimates, that’s the equivalent of six to nine months of additional schooling, without any added instruction time or teacher workload. Instead, the AI helped tailor the learning to the student. This fits other work on AI tutoring, suggesting that customized tutors can significantly boost learning when used properly.

This is a relatively small difference in how you use AI and yet it leads to big outcome differences. Worse, human nature leads us to make the wrong choices. Learning requires us to face our own ignorance and do hard intellectual work, and these things are really uncomfortable. Which is why students rate entertaining lectures as more educational than doing hard problems in class, even though they actually learn more from the hard work. To benefit from AI in learning you need to pivot from using AI to solve problems, to pushing you to solve problems yourself.

Fortunately, the three major AI companies have tools that provide at least some support for learning by making the AI act more like a tutor. Unfortunately, they are not intuitive to access. Gemini is the easiest. Hit plus and pick Guided Learning. For ChatGPT, you need to type “/learn” into the chatbox. For Claude, you need to hit the plus, select use style, and select “learning” (Anthropic has announced that this approach is changing but has not yet documented the change). In all cases, you should use a thinking or advanced model where possible, especially for STEM subjects. And these modes will only help support someone who wants to learn, they won’t stop you from cheating if you want.

Too frictionless

AI need not undermine your ability to think, but it can do so if used badly and badly is often the default. My colleagues at Wharton call this “cognitive surrender,” and they documented how people would stop thinking about problems and just let the AI do the work, even when the AI was wrong. I think part of the problem is the way these tools are designed.

When AI systems required elaborate back-and-forth conversations and made errors frequently, humans had to be engaged at every step. Agentic systems are designed to make your life easier, because they just do stuff. Which is great for getting stuff done, bad for learning anything, or staying authentic, or avoiding cognitive surrender. If you put in a hard request and get an answer, it is tempting to just go with the AI’s response.

In our recently published paper with Fabrizio Dell’Acqua and my colleagues at Harvard, MIT, the University of Warwick, BCG, and elsewhere (which I wrote about here three years ago, but publishing academic work takes a while!) we ran an experiment on 758 consultants at Boston Consulting Group, half of whom got access to GPT-4. Consultants using AI vastly outperformed those without. But we also asked consultants to do solve a problem that we knew the AI would fail at. Consultants using AI on this task were significantly less likely to get the right answer than consultants without it. The AI gave them an authoritative-looking answer that happened to be incorrect, and most of them, the same elite consultants who outperformed on everything else, did not catch it. Of course, now AI just solves that problem, so the issue isn’t really error rates now, it is failing to learn how to be a good consultant by giving into the same impulse to surrender.

Again, this does not have to be the default. In a small study conducted by Anthropic, programmers used AI to help them do a new task. Those who just let the AI do the work couldn’t answer questions about what they had done, a sign of surrender. But people who asked the AI to explain what it was doing, or those who used AI to help them with only some of the work, seemed to avoid that fate.

Some of the solution might be in the tools themselves, but that is limited. A version of ChatGPT that asked, before every answer, “would you rather I push you to think through this, or just give it to you?” or told you “I think this would be more authentic if you wrote this” would be insufferable most of the time. But there are places where we absolutely need these reminders. The Taipei result hints at one direction, namely system-level constraints rather than user-level willpower, but we don’t see much of that in the consumer products, and the commercial pressure mostly pushes in the opposite direction.

Choosing what to keep human

A lot of the problem is going to come down to us. To be clear, I am cool with a lot of cognitive surrender. I don’t remember phone numbers anymore because my phone does that for me. I am happy my kids didn’t need to learn cursive. I am fine with calculators doing my daily math and my computer figuring out how to schedule my classes. These were once useful skills, but we were probably right to get rid of them.

AI is different because the technology is general enough that virtually any cognitive task can be offloaded into it to some degree. I don’t want to be too precious about writing: there is no principle that says a polished email draft has to come out of a human mind any more than a column of arithmetic has to. But we don’t want to give up everything, and that we mostly don’t know yet, for any specific task, what is important and what is not. Deciding that is going to be a real challenge.

The point isn't to avoid AI but to be intentional about it by making a conscious choice about AI use, rather than reflexive dependence or reflexive avoidance. More broadly, we are at the point where the defaults are being set for what kind of work to give AI: by the AI companies designing for frictionless use, by employers deciding what counts as “using AI well,” and by people teaching the ever-shifting concept of “AI literacy.” A lot of this is happening without, ironically, any real planning or consideration. And I suspect it will be hard to reverse these defaults once a generation of workers and students has built habits around them. The most important thing we can do is keep asking what to hand over and what to keep for ourselves… and not expect anyone, including the AI, to answer that for us.

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16th June 2026 I can 100% attest to the fact that Qwen3.6-27B is a very capable local model for coding tasks. Over the last month and a half I've been using it almost daily, either on my M2 Ultra or on my RTX 5090 box. I use it for small mundane tasks at ggml-org - nothing really impressive, but definitely a helpful tool for a maintainer. I think I would be using it much more, if I didn't have to spend a lot of my time on reviewing PRs. Currently, I have a very lightweight harness - the pi agent with everything stripped ( pi -nc --offline ) and a short system prompt to align it a bit with my style. — Georgi Gerganov, Hacker News comment on Running local models is good now by Boykis Recent articles - sqlite-utils 4.0rc1 adds migrations and nested transactions - 21st June 2026 - Datasette Apps: Host custom HTML applications inside Datasette - 18th June 2026 - GLM-5.2 is probably the most powerful text-only open weights LLM - 17th June 2026