# fast Published articles for fast. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Power Banks Make You Pick a Size, Trozk T3 Lets You Build One DevFeed: [Power Banks Make You Pick a Size, Trozk T3 Lets You Build One]() Original publisher: [Read original article]() Author: JC Torres Published: 2026-09-17T13:20:55Z Content type: article Language: en Sources: [Yanko Design]() Topics: [Hardware]() Tags: [battery](), [capacity](), [fast](), [gadgets](), [gadgets-technology-modular-power-bank](), [hardware](), [modular](), [power](), [power-bank](), [technology]() ### AI overview The Trozk T3 Module Power Bank uses separate modules that can be combined or detached to provide 5,000mAh, 10,000mAh, or 15,000mAh of capacity. It supports charging up to 22.5W through PD and QC protocols and includes built-in safety protections, automotive-grade 21700 cells, and a color status display. ### Source excerpt Power Banks Make You Pick a Size, Trozk T3 Lets You Build One Most power banks force a single decision at checkout: buy the huge one for peace of mind, or the tiny one for your pocket, and... ## Small Models Have Arrived DevFeed: [Small Models Have Arrived]() Original publisher: [Read original article]() Author: Calvin French-Owen Published: 2026-08-26T12:00:00Z Content type: opinion Language: en Sources: [Calvin French-Owen]() Topics: [Artificial Intelligence](), [Inference](), [Users]() Tags: [ai](), [business](), [consumer](), [cost](), [fast](), [models]() ### AI overview The article argues that fast, inexpensive, capable small models have improved enough to make consumer AI applications more viable. It also suggests these models can handle much of the responsive, incremental work that businesses perform, while reducing inference costs. ### Source excerpt The fast/cheap/good-enough models have quietly gotten good. That unlocks consumer AI, and most of the "token spewer" work that actually runs a business. ## Autocomplete Search with the Element API & VueJS DevFeed: [Autocomplete Search with the Element API & VueJS]() Original publisher: [Read original article]() Author: andrew@nystudio107.com (Andrew Welch) Published: 2017-02-22T06:19:00Z Content type: tutorial Language: en Sources: [nystudio107 | Articles on modern web development.]() Topics: [Vue.js](), [Content Management System](), [API](), [Ajax](), [JSON](), [npm](), [Front end](), [GitHub]() Tags: [api](), [autocomplete](), [cms](), [cmss](), [craft](), [create](), [element](), [fast](), [feature](), [functional](), [github](), [heres](), [insights](), [json](), [npm](), [search](), [vuejs]() ### AI overview A tutorial on using Craft CMS's Element API and VueJS to build an autocomplete search feature for blog entries. The Vue component sends AJAX requests to the Element API, receives JSON results, and renders them in a navigable dropdown. ### Source excerpt Here's how to use Craft CMS's Element API and VueJS to create a fast, functional autocomplete search feature ## Abbreviated keys for numeric to accelerate numeric sorts DevFeed: [Abbreviated keys for numeric to accelerate numeric sorts]() Original publisher: [Read original article]() Author: Peter Geoghegan (noreply@blogger.com) Published: 2015-04-04T16:19:00Z Content type: article Language: en Sources: [Peter Geoghegan's blog]() Topics: [PostgreSQL](), [Sorting](), [Development]() Tags: [abbreviation](), [benchmarks](), [c](), [cardinality](), [commit](), [count](), [fast](), [improvements](), [indexes](), [internals](), [maintenance](), [postgresql](), [sorting](), [table]() ### AI overview The article discusses PostgreSQL numeric abbreviated keys, a committed patch intended to accelerate numeric sorting. It reports 6x-7x improvements for representative in-memory queries and a 12x improvement in a PostgreSQL 9.5 text-column CREATE INDEX benchmark compared with PostgreSQL 9.4. ### Source excerpt Andrew Gierth's numeric abbreviated keys patch was committed recently. This commit added abbreviation/sortsupport for the numeric type (the PostgreSQL type which allows practically arbitrary precision, typically recommended for representing monetary values). The encoding scheme that Andrew came up with is rather clever - it has an excellent tendency to concentrate entropy from the original values into the generated abbreviated keys in real world cases. As far as accelerating sorts goes, numeric abbreviation is at least as effective as the original text abbreviation scheme. I easily saw improvements of 6x-7x with representative queries that did not spill to disk (i.e. that used quicksort). In essence, the patch makes sorting numeric values almost as cheap as sorting simple integers, since that is often all that is actually required during sorting proper (the abbreviated keys compare as integers, except that the comparison is inverted to comport with how abbreviation builds abbreviated values from numerics as tuples are copied into local memory ahead of sorting - see the patch for exact details). Separately, over lunch at pgConf.US in New York, Corey Huinker complained about a slow, routine data warehousing CREATE INDEX operation that took far too long. The indexes in question were built on a single text column. I suggested that Corey check out how PostgreSQL 9.5 performs, where this operation is accelerated by text abbreviation, often very effectively. Corey chose an organic set of data that could be taken as a reasonable proxy for how PostgreSQL behaves when he performs these routine index builds. In all cases maintenance_work_mem was set to 64MB, meaning that an external tapesort is always required - those details were consistent. This was a table with 18 million rows. Apparently, on PostgreSQL 9.4, without abbreviation, the CREATE INDEX took 10 minutes and 19 seconds in total. On PostgreSQL 9.5, with identical settings, it took only 51.3 seconds - a 12x improvemen ## P vs. NP, A Primer (And a Proof Written in Racket) DevFeed: [P vs. NP, A Primer (And a Proof Written in Racket)]() Original publisher: [Read original article]() Published: 2012-02-23T19:57:35Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Computing](), [Algorithms, Complexity](), [Racket](), [Programming]() Tags: [algorithm](), [algorithms](), [analysis](), [big-o-notation](), [class](), [computational-complexity](), [computing](), [decidability](), [efficiency](), [fast](), [halting-problem](), [input](), [language](), [machine](), [np-completeness](), [p-versus-np](), [primer](), [racket](), [solve](), [theory](), [turing-machine](), [turing-machines]() ### AI overview A primer on the distinction between decidability and computational efficiency, introducing the complexity class P and its definition in terms of polynomial-time decision by a Turing machine. It also discusses efficient algorithms and gives a card-sorting example. ### Source excerpt Decidability Versus Efficiency In the early days of computing theory, the important questions were primarily about decidability. What sorts of problems are beyond the power of a Turing machine to solve? As we saw in our last primer on Turing machines, the halting problem is such an example: it can never be solved a finite amount of time by a Turing machine. However, more recently (in the past half-century) the focus of computing theory has shifted away from possibility in favor of determining feasibility.