AI Token Cost Optimizer for Dev Teams

The framework, developed with Superstate, Securitize and Dowgo, lets regulated funds and securities trade on Uniswap while enforcing compliance rules necessary for institutions.

Input Output will transfer control of the Haskell node, Plutus, Hydra and other components as founder Charles Hoskinson says the network must change and start growing again.

Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires. They often follow a maximum-context-first strategy--re-reading files and dependencies they have already seen--turning a one-line edit into a small code-base audit. We argue the missing capability is task-aware execution-scope estimation: judging a task's difficulty, the information it truly needs, and the shortest reliable path before committing budget. We formalize minimum-sufficient execution and the Agent Cognitive Redundancy Ratio (ACRR), and propose E3 (Estimate, Execute, Expand): the agent estimates an initial operating point, executes a minimum viable path, and expands scope only when verification fails. On MSE-Bench--a deterministic benchmark of 121 edits in a capability-controlled simulator--E3 matches the strongest baseline's 100% success while cutting cost by 85%, tokens by 91%, and inspected files by 92%, and further beats a strong adaptive retrieval baseline by 16%; the gains survive held-out instruction wording and essentially every cost weighting. A companion real-model harness (LLM-Case) corroborates the effect on a live gpt-4o agent editing a real open-source library, with every candidate patch graded by actually running the project's real pytest suite against a measured oracle: the over-reading is milder but real, and E3 is the leanest and fastest policy at comparable task success--its one shortfall a provider rate-limit, not a wrong edit. We frame this as a controlled probe of execution redundancy, not a measurement of any deployed agent, and position task-aware execution as a step toward engineering-grounded AI (EGAI)--agents whose effort is anchored in the engineering reality of the task. We release the framework and benchmark.

Plan evaluators can reward a strategic plan for becoming less explicit. This paper studies that failure in a staged expected-value scorer for LLM-generated venture routes. Proposition 1 gives the score change from deleting an interior transition while retargeting its predecessor and retaining downstream value: Delta_k = (prod_{i<k} p_i)[c_k + (1 - p_k)R_{k+1}]. On a frozen 26-route cohort, all 57 admissible deletions matched the analytic identity and threshold sign, and every route had at least one score-improving deletion. A score-seeking optimizer, allowed to restructure routes but not told the exploit mechanism, found baseline-beating uncovered structures in 21/26 routes. GATE refused score release for 26/26 silenced routes with 0/26 honest suspensions; after refusal, 47/54 next revisions repaired to a covered structure, and strict covered improvement rose from 1/26 to 13/26. An adaptive compiler-aware co-author exposed the registry-provenance boundary: obligation-channel evasions remained 6/6 across all four v1/v1.5 conditions, while delta-indexed cost floors reduced beat-honest routes from 6/6 to 3/6 and fundability-by-silence from 5/6 to 0/6 without establishing semantic completeness. If a plan scores better only because it omits necessary work, the plan did not improve; the evaluation created an omission incentive. PCSC detects and neutralizes post-hoc omission splices over model-mediated typed-state records. In the cooperative setting tested, GATE acts as a deterministic search-shaping constraint, not merely a post-hoc filter. It does not verify the semantic completeness or real-world quality of arbitrary LLM-generated strategies.

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

<p>This past Saturday, <a href="https://lobste.rs/~pushcx" rel="ugc">@pushcx</a> and I deployed the <a href="https://github.com/lobsters/lobsters/pull/1927" rel="ugc">SQLite pull request</a> to production. We were waiting till this morning to see how it would react to the Monday traffic spike before making this post. Needless to say, SQLite seems to have passed with flying colors: cpu usage is down, memory usage is down, site seems to be snappier at least for me, 1/2 the vps cost once mariadb vps is taken down, and finally <a href="https://gist.github.com/pushcx/eb7cdf2dc9707dc3ab9e7173d197ddfc?permalink_comment_id=6251610#gistcomment-6251610" rel="ugc">"We're having a quiet Monday."</a>. Finally <a href="https://github.com/lobsters/lobsters/issues/539#issuecomment-4959857588" rel="ugc">#539 Migrate to SQLite</a> was closed this morning.</p> <p>Let us know if you have any questions about the migration.</p> <p>Background Story:</p> <p>I got involved with this migration because back in 2019 I stumbled upon <a href="https://github.com/lobsters/lobsters/issues/539" rel="ugc">#539</a> and because I had lots of experience working with, managing and migrating largish databases, I left a <a href="https://github.com/lobsters/lobsters/issues/539#issuecomment-509041532" rel="ugc">comment</a> suggesting MySQL as an alternative, because of the compatibility between MariaDB and MySQL. At that time I wasn't planning on getting involved since there were already conversations in place to migrate to PostgreSQL.</p> <p>Fast forward to 2025, <a href="https://github.com/lobsters/lobsters/issues/539#issuecomment-2604683262" rel="ugc">Rahul left a comment</a> mentioning K1's acquisition of MariaDB. A discussion around the details of migrating to postgresql proceeded. Then in February, <a href="https://github.com/lobsters/lobsters/issues/539#issuecomment-2640076884" rel="ugc">Rahul asked "Can lobsters run on sqlite?</a> which included a very detailed post around SQLite.</p> <p>I officially showed interest in taking on this project in <a href="https://github.com/lobsters/lobsters/issues/539#issuecomment-2956952922" rel="ugc">June 2025</a>. I think this somehow got mentioned in <a href="https://lobste.rs/s/zzprkr/weekly_lobsters_office_hours" rel="ugc">lobsters office hours</a> but it has been so long since then that I don't rememeber for certain.</p> <p>In <a href="https://github.com/lobsters/lobsters/issues/539#issuecomment-3198784051" rel="ugc">August 2025</a> I opened my <a href="https://github.com/lobsters/lobsters/pull/1705" rel="ugc">first pull request attempt</a> when I got busy and couldn't attend to the PR. Github closed it as stale and I couldn't reopen it so I opened another PR. The <a href="https://github.com/lobsters/lobsters/pull/1871" rel="ugc">second PR attempt</a> included some performance testing, a <a href="https://github.com/lobsters/lobsters/blob/main/lib/tasks/migrate.rake" rel="ugc">database x to database y script</a> (since none of the existing mariadb/mysql to sqlite scripts satisfied me), debugging and thinking around data integrity.</p> <p>Then came the first deploy on Feb 21st. <a href="https://lobste.rs/~pushcx" rel="ugc">@pushcx</a> and I got on a call, came up with a <a href="https://gist.github.com/pushcx/eb7cdf2dc9707dc3ab9e7173d197ddfc/a11e6d5865776f3bb4e1adbc56582a3b1f99878c" rel="ugc">checklist</a> for the deployment. Everything went right up until the deployment of the PR. Once deployed the site was in readonly mode, but just the readonly traffic was spiking all the cpus to 100%. We couldn't figure out what the problem was so we decided to <a href="https://github.com/lobsters/lobsters/pull/1924" rel="ugc">revert</a>. I didn't feel great after that first failed deploy since I knew that performance could be a problem due to not having access to the production database.</p> <p>Two days after the failed deploy I opened the <a href="https://github.com/lobsters/lobsters/pull/1927" rel="ugc">3rd and final pr attempt</a>. I fixed some <a href="https://github.com/lobsters/lobsters/pull/1927/changes/e7630ffe5c0df9fc0a388db5b6b6e11eb4a4c407" rel="ugc">minor issues with search</a> that were discovered during the failed deploy, created a <a href="https://github.com/lobsters/lobsters/pull/1927/changes/c3c12e6f134c9d67c37c57a0390f9d3a2c9f26d8" rel="ugc">bulk data creation script</a> which took a week to get half of lobsters' data set size created locally, and committed the three changes that fixed the performance issues during the first deploy: <a href="https://github.com/lobsters/lobsters/pull/1927/changes/14a8c5f412cc23c887a5b809453596c02d8df6d1" rel="ugc">1</a>, <a href="https://github.com/lobsters/lobsters/pull/1927/changes/4dc58fc1d98ed393c4b447c71568bc0cb700896b" rel="ugc">2</a>, <a href="https://github.com/lobsters/lobsters/pull/1927/changes/4476ae3e50602b5e0d7805fef40fc94a81daaa39" rel="ugc">3</a>. The performance issues boiled down to SQLite doing full table scans on the largest tables in the database for 2 of queries and the 3rd one solved an n+1 issue. During the morning of the second deployment, I also added a <a href="https://github.com/lobsters/lobsters/pull/1927/changes/a62fc572c068f7df45ade8a5fad117dafc68cc00" rel="ugc">slow query log</a> just in case there were more performance issues during the deployment.</p> <p>Then came the second deploy on July 11th. <a href="https://lobste.rs/~pushcx" rel="ugc">@pushcx</a> and I got on a morning call and came up with a deployment and revert checklist. Everything was going smoothly and then <a href="https://lobste.rs/~pushcx" rel="ugc">@pushcx</a> merged and deployed the PR. Once deployed the site was still live and the cpu/memory usage was still good. This was a big relief for me. We monitored site metrics and irc for people mentioning issues, which a couple did and those were promptly fixed: <a href="https://github.com/lobsters/lobsters/commit/1dd9d5e4d7ecc7f924a88c8792ec44f7c90d4691" rel="ugc">1</a>, <a href="https://github.com/lobsters/lobsters/commit/e88a07e1f121cbae772340c29e482399b1e92b75" rel="ugc">2</a>. Overall the site seemed to work so we wrapped up the call and waited till Monday when the traffic spikes.</p> <p>Monday came and the site is stil happy so we're calling this a win and moving on.</p> <p>SQLite lessons:</p> <ol> <li>The SQLite gem supports user defined functions (udfs) and we used it to implement some missing functions in SQLite like <a href="https://github.com/lobsters/lobsters/blob/main/config/initializers/sqlite_functions.rb" rel="ugc">regexp, if and stddev</a> so that we wouldn't have to deal with too many sql migration workarounds.</li> <li>SQLite doesn't support unsigned bigints. Previously, the mariadb was using unsigned bigints for certain ids, so we had to switch those to bigints for the migration.</li> <li>Collation in SQLite is rather weak compared to MariaDB. Lobste.rs used utf8mb4_general_ci in MariaDB, but used <a href="https://www.sqlite.org/datatype3.html#collating_sequences" rel="ugc">NOCASE</a> in SQLite. The downside of NOCASE is that it only supports ASCII characters, not the full UTF case folding.</li> <li>Use the preferred <a href="https://www.sqlite.org/fts5.html#contentless_delete_tables" rel="ugc">Contentless-Delete Tables in SQLite</a> for your full text search tables. These are not the default. I'm constantly surprised by the default choices of SQLite.</li> </ol> <p>Rails lessons:</p> <ol> <li>The <a href="https://github.com/rails/rails/blob/v8.0.5/activerecord/lib/active_record/connection_adapters/sqlite3_adapter.rb#L84-L91" rel="ugc">default PRAGMAs in Rails</a> seem to be working for lobsters.</li> <li>You typically don't think about it but database migrations are database specific. I had to move the old migrations out to an <a href="https://github.com/lobsters/lobsters/tree/main/db/old_migrations" rel="ugc">old migrations</a> directory so that db:migrate would continue to work.</li> </ol> <p>Lobsters codebase lessons:</p> <ol> <li>There is a <a href="https://github.com/lobsters/lobsters/blob/main/app/models/search_parser.rb" rel="ugc">search parser</a> in the lobsters codebase.</li> <li>I learned about <a href="https://github.com/lobsters/lobsters/blob/main/config/initializers/heinous_inline_partial.rb" rel="ugc">heinous_inline_partials</a> which is a hack to speed up rendering.</li> <li>The lobsters testsuite was essential in making sure I could migrate to SQLite without a ton of manual testing.</li> </ol> <p>Overall lessons:</p> <ol> <li>I think a key ingredient in making this work was good communication from everyone that participated. I don't think this would have been possible otherwise.</li> <li>Migrating the underlying database without having access to the production database is really hard to get right. This was my first underlying database migration without having access to production. One lesson I'll take away from this is that I'll make sure to have realistic dataset sizes before doing another underlying database migration in the future.</li> </ol> <p>Wishes:</p> <ol> <li>I wish we could say in a test, "Fail if you encounter any full table scans". Which would have caught the perf issues we experienced during the first deploy.</li> <li>I wish creating a production like dataset would be much easier than having to manually write something and waiting a week.</li> </ol>

Low-Rank Adaptation (LoRA) has become a widely adopted technique for efficient neural network fine-tuning, decomposing model updates into low-rank matrices. However, LoRA remains computationally costly because it updates all matrices uniformly, regardless of their actual contribution to adaptation. This cost is especially prohibitive for large-scale models with billions of parameters and for resource-constrained settings such as edge deployment and on-device fine-tuning. We show for the first time that not all LoRA matrices are equally worth tuning: matrices with smaller condition numbers (the ratio of largest to smallest singular value) are already well-balanced across directions and contribute only marginally to adaptation, whereas matrices with larger condition numbers contain underdeveloped directions that span richer subspaces and drive most of the performance gains. This observation itself is a key contribution of our work, and it motivates a more selective approach to fine-tuning. Building on this insight, we propose \k{appa}-LoRA, a method that optimizes LoRA by focusing updates on the matrices with the largest condition numbers, which capture the most informative directions of change. By restricting LoRA updates to the top 50% of weight matrices ranked by condition number, \k{appa}-LoRA halves the trainable parameter count and correspondingly reduces compute and memory cost. Extensive experiments across multiple benchmarks show that this design cuts fine-tuning time by 16.2% on average while matching the accuracy of standard LoRA and reducing memory cost by 4.5%. Further analysis reveals that the condition numbers of the selected matrices consistently decrease over training, suggesting that \k{appa}-LoRA's effectiveness stems from targeted spectral rebalancing rather than parameter selection alone.

Part 1 of "Multi-Agent Systems in Production: What They Don't Tell You" — a four-part series...

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

Neural surrogate models offer fast approximate mappings from PDE parameters to solutions, but they typically treat solving as a purely statistical task: once trained, they struggle to correct their own constraint violations and extrapolate beyond the training distribution. Recent hybrid methods promote physical correctness by targeting the PDE residual via gradient descent or Gauss--Newton steps, but inherit the compute cost and instability of the underlying classical optimizers. We show, theoretically and empirically, that numerically minimizing the PDE residual can be an unreliable proxy for reconstruction accuracy in ill-conditioned systems, explaining why these methods often do not make accurate predictions despite achieving low residuals. We propose error-conditioned Neural Solvers (ENS), built on a different principle: rather than an optimization target, the PDE residual field is passed as a direct input to the network at each iteration, enabling it to read the spatial structure of its own errors and learn an update policy to iteratively correct its predictions. Across four PDE families, ENS attains the highest prediction accuracy in the large majority of settings, with gains reaching $10\times$ on turbulent Kolmogorov flow, while avoiding the expensive compute cost of hybrid methods. ENS's learned correction policy generalizes under distribution shift, including zero-shot parameter changes and cross-equation transfer, where its relative advantage is largest in the ill-conditioned regimes where residual minimization is least reliable. Project website: https://neuralsolver.github.io/.

About a year ago, I turned my gaming PC into a local AI Lab. And yes, the most important word in that...