We initially tackled this by training SLMs for specialized use cases. This approach gave birth to JigsawStack: one model for per hyper-focused task, bundled with the necessary infrastructure.

JigsawStack Feedback

Speaking with customers, we identified three main challenges with JigsawStack:

• Flexibility: Developers expect fully flexible, unstructured inputs via prompts, alongside structured, developer-defined outputs. Currently, multi-step processes require manual setup. For example, extracting and translating text requires two separate API calls and models.

• Integrability: Because JigsawStack uses custom API schemas, integrating and maintaining it with existing AI SDKs (like LangChain) is difficult.

• Research and Cost: Each model requires its own training pipeline, infrastructure, and research efforts.

We spent over 8 months researching and building Interfaze to solve these exact problems. You can read our paper here: https://arxiv.org/abs/2602.04101

The solution: Interfaze

Interfaze outperforms JigsawStack and generalized LLMs in deterministic developer across all modalities: text, vision, audio, and multilingual tasks. Check out our landing page for benchmarks. Beyond better results, Interfaze offers:

• Flexibility: We support the OpenAI schema out of the box. You can prompt it like any LLM, but you get higher-quality output and additional structured metadata standard LLMs can't provide. Also works with any AI SDK like LangChain.

• Multi-turn calls in one request: You can now extract and translate a document in a single prompt while defining your desired output structure.

• More metadata: Additional metadata produced for tasks like OCR, scraping and more. Check out pre-context to learn more.

• Shared Architecture: Research improvements are shared across tasks. For example, enhancing language support for diarization in STT also improves OCR.

• Backward Compatibility: You can get the exact same outputs as JigsawStack directly within Interfaze using Run task.

JigsawStack & Interfaze Updates

JigsawStack isn't going anywhere. Much of the Interfaze infrastructure is built on top of it. For those needing smaller models running on-premise on a single H100, JigsawStack will continue to power them with constant upgrades and fixes.

However, since most self-serve users are now on Interfaze, we are shifting our branding focus to Interfaze across all socials and emails. Our official company entity will remain JigsawStack.

This transition will happen progressively over the next few days with zero downtime or effects on your systems and APIs.

Upcoming Changes:

• Branding updates from JigsawStack to Interfaze.

• Marketing content will be around Interfaze.

• Interfaze support added to open-source applications like our Translation widget and Google Sheets integration.

Our support team, SOC2 compliance, agreements, and licenses remain unchanged across both Interfaze and JigsawStack.

If you have any questions or want to migrate to Interfaze, Feel free to reach out to https://jigsawstack.com/help :)

In this context, we aim to evaluate where our service stands today, in light of recent research-driven advances, new model releases, and our own internal improvements to JigsawStack Translation.

What Makes a Great Translation Solution?

Translation models should maintain consistency in grammar, word order, and syntax across languages while preserving meaning, intent, and contextual relationships without introducing omissions, distortions, or unintended reinterpretations. We tested ChatGPT Translate, Translate Gemma 27b, Google Translation API & JigsawStack using these five categories to evaluate where models break.

1. Long-Form Legal Sentences
   Testing preservation of sentence boundaries, clause hierarchy, tense flow, and referential integrity.

2. Grammar & Ambiguity
   Evaluating how models resolve structurally or semantically ambiguous constructions without hallucinating clarity or altering intent.

3. Numbers, Dates, Identifiers & Entities
   Verifying exact retention of numeric values, temporal references, legal identifiers, and named entities without normalisation, reformatting, or substitution.

4. RTL and LTR Language Pairs
   Assessing stability when translating between right-to-left and left-to-right scripts, including punctuation direction, token ordering, and structural alignment.

5. Slang & Informal Language
   Measuring how models handle colloquial expressions, idioms, and non-standard grammar without sanitising tone or over-formalising the output.

Sneak peek if you can’t wait 👇

◐ = partial | ❌ = inaccurate/fails | ✅ = accurate/succeeds

Key Findings:

• JigsawStack was solid, performed best overall with an easy-to-use API and a generous free tier rate limit, and it stands as the best among all tested.

• ChatGPT Translate performed well overall, particularly in preserving structure and identifiers. However, we observed a small number of edge cases where the system shifts into an explanation mode rather than producing a direct translation.

• Google Translate API over-simplified grammar and transliterated brand names unnecessarily

• Translate Gemma 27b exhibited the highest error rate among the evaluated models, including hallucinations such as introducing unrelated context (e.g., music references or “final version” labels). When direct slang or sensitive terms were present in the input, the model frequently refused to generate a translation altogether.

Results

Category 1: Long-Form Legal Sentences

To evaluate this category, we take the following English source text and analyse its translation into Hindi, a language written in a non-Latin script.

Input Text -

This Court already required Musk to elect between his unjust enrichment and breach of contract claims. Dkt. 298. OpenAI now demands that Musk elect between his unjust enrichment and tort claims, too. That demand is baseless. The unjust enrichment and tort claims have different elements, and Musk may present both theories to the jury at trial. Where different claims have different elements, the plaintiff may present both claims to the jury, and the jury can then decide which claim or claims are supported by the evidence. A court may not force upon the plaintiff an election between causes of action that the plaintiff has a right to plead. The system operates in three phases. First, data is collected from distributed nodes. Second, the data is validated and normalized. Finally, the processed data is indexed and stored. Failure in the second phase prevents progression to the third. The policy applies to all users who access the platform after January 1st. Users who continue using the service implicitly agree to the updated terms. Any violation may result in suspension or termination of access.

Expected Text -

इस न्यायालय ने पहले ही मस्क को अपने अनुचित समृद्धि और अनुबंध के उल्लंघन के दावों के बीच चयन करने का निर्देश दिया था। Dkt. 298. अब OpenAI यह मांग कर रहा है कि मस्क अपने अनुचित समृद्धि और अपकृत्य दावों के बीच भी चयन करे। यह मांग निराधार है। अनुचित समृद्धि और अपकृत्य दावों के तत्व अलग-अलग हैं, और मस्क मुकदमे में जूरी के समक्ष दोनों सिद्धांत प्रस्तुत कर सकता है। जहाँ विभिन्न दावों के तत्व भिन्न होते हैं, वहाँ वादी दोनों दावे जूरी के समक्ष रख सकता है, और जूरी यह तय कर सकती है कि कौन-सा दावा या दावे साक्ष्यों द्वारा समर्थित हैं। न्यायालय वादी पर उन कारणों के बीच चयन थोप नहीं सकता जिन्हें प्रस्तुत करने का अधिकार वादी को प्राप्त है।प्रणाली तीन चरणों में संचालित होती है। प्रथम, डेटा को वितरित नोड्स से एकत्र किया जाता है। द्वितीय, डेटा का सत्यापन और सामान्यीकरण किया जाता है। अंततः, संसाधित डेटा को अनुक्रमित कर संग्रहित किया जाता है। द्वितीय चरण में विफलता होने पर तृतीय चरण में प्रगति संभव नहीं होती।\n\nयह नीति 1 जनवरी के बाद मंच का उपयोग करने वाले सभी उपयोगकर्ताओं पर लागू होती है। जो उपयोगकर्ता सेवा का उपयोग जारी रखते हैं, वे अद्यतन शर्तों से निहित रूप से सहमत माने जाते हैं। किसी भी उल्लंघन के परिणामस्वरूप पहुँच का निलंबन या समाप्ति हो सकती है।

Response - ChatGPT Translate

यह न्यायालय पहले ही मस्क को उनके अनुचित लाभ और अनुबंध के उल्लंघन के दावों में से एक चुनने की आवश्यकता बता चुका है। Dkt. 298. अब OpenAI मांग कर रहा है कि मस्क अपने अनुचित लाभ और टॉर्ट दावों में से भी चुनाव करें। यह मांग निराधार है। अनुचित लाभ और टॉर्ट दावों के तत्व अलग-अलग हैं, और मस्क मुकदमे के दौरान जूरी के समक्ष दोनों सिद्धांत प्रस्तुत कर सकते हैं। जहाँ अलग-अलग दावों के तत्व अलग होते हैं, वहाँ वादी दोनों दावे जूरी के सामने रख सकता है, और जूरी तब यह तय कर सकती है कि कौन-सा दावा या दावे साक्ष्यों द्वारा समर्थित हैं। कोई न्यायालय वादी पर ऐसे कारण-ए-दावा के बीच चुनाव थोप नहीं सकता जिन्हें प्रस्तुत करने का अधिकार वादी को है। प्रणाली तीन चरणों में कार्य करती है। पहले, वितरित नोड्स से डेटा एकत्र किया जाता है। दूसरे, डेटा का सत्यापन और सामान्यीकरण किया जाता है। अंत में, संसाधित डेटा को अनुक्रमित कर संग्रहीत किया जाता है। दूसरे चरण में विफलता तीसरे चरण में प्रगति को रोक देती है। यह नीति 1 जनवरी के बाद प्लेटफ़ॉर्म तक पहुँचने वाले सभी उपयोगकर्ताओं पर लागू होती है। जो उपयोगकर्ता सेवा का उपयोग जारी रखते हैं, वे अप्रत्यक्ष रूप से अद्यतन शर्तों से सहमत होते हैं। किसी भी उल्लंघन के परिणामस्वरूप पहुँच का निलंबन या समाप्ति हो सकती है।

Response - Translate Gemma 27b

यह न्यायालय पहले ही मस्क को उनके अनुचित लाभ और अनुबंध उल्लंघन के दावों में से किसी एक को चुनने का आदेश दे चुका है। दस्तावेज़ संख्या 298। अब ओपनएआई मांग कर रहा है कि मस्क अपने अनुचित लाभ और क्षतिपूर्ति के दावों में से किसी एक को चुनें। यह मांग निराधार है। अनुचित लाभ और क्षतिपूर्ति के दावों में अलग-अलग तत्व होते हैं, और मस्क दोनों सिद्धांतों को मुकदमे के दौरान जूरी के सामने पेश कर सकते हैं। जब अलग-अलग दावों में अलग-अलग तत्व होते हैं, तो वादी दोनों दावों को जूरी के सामने पेश कर सकता है, और फिर जूरी यह तय कर सकती है कि कौन सा दावा या दावे सबूतों द्वारा समर्थित हैं। कोई न्यायालय वादी पर उन कारणों में से किसी एक को चुनने के लिए दबाव नहीं डाल सकता, जिनका दावा करने का वादी को अधिकार है। यह प्रणाली तीन चरणों में काम करती है। सबसे पहले, वितरित नोड्स से डेटा एकत्र किया जाता है। दूसरे, डेटा को मान्य और सामान्यीकृत किया जाता है। अंत में, संसाधित डेटा को अनुक्रमित और संग्रहीत किया जाता है। दूसरे चरण में विफलता तीसरे चरण में प्रगति को रोकती है। यह नीति उन सभी उपयोगकर्ताओं पर लागू होती है जो 1 जनवरी के बाद प्लेटफ़ॉर्म का उपयोग करते हैं। जो उपयोगकर्ता सेवा का उपयोग जारी रखते हैं, वे निहित रूप से अद्यतन शर्तों से सहमत होते हैं। किसी भी उल्लंघन के परिणामस्वरूप पहुंच निलंबित या समाप्त की जा सकती है।

Notes - Translated "Dkt. 298" to "दस्तावेज़ संख्या 298" (document number) - should be preserved as-is. Mistranslated "tort" as "क्षतिपूर्ति" (compensation), which is incorrect.

Response - Google Translation API

इस न्यायालय ने पहले ही मस्क को उनके अनुचित लाभ और अनुबंध उल्लंघन के दावों में से किसी एक को चुनने का निर्देश दिया था। (डॉक्टर संख्या 298)। अब ओपनएआई मस्क से उनके अनुचित लाभ और अपकृत्य दावों में से किसी एक को चुनने की मांग कर रहा है। यह मांग निराधार है। अनुचित लाभ और अपकृत्य दावों के तत्व अलग-अलग हैं, और मस्क मुकदमे के दौरान जूरी के समक्ष दोनों सिद्धांत प्रस्तुत कर सकते हैं। जहां विभिन्न दावों के तत्व अलग-अलग होते हैं, वहां वादी जूरी के समक्ष दोनों दावे प्रस्तुत कर सकता है, और जूरी तब यह तय कर सकती है कि कौन सा दावा या दावे साक्ष्यों द्वारा समर्थित हैं। कोई न्यायालय वादी को उन वादों में से किसी एक को चुनने के लिए बाध्य नहीं कर सकता जिन्हें उठाने का वादी को अधिकार है। यह प्रणाली तीन चरणों में कार्य करती है। पहला, वितरित नोड्स से डेटा एकत्र किया जाता है। दूसरा, डेटा का सत्यापन और मानकीकरण किया जाता है। अंत में, संसाधित डेटा को अनुक्रमित और संग्रहीत किया जाता है। दूसरे चरण में विफलता तीसरे चरण में प्रगति को रोकती है। यह नीति 1 जनवरी के बाद प्लेटफॉर्म का उपयोग करने वाले सभी उपयोगकर्ताओं पर लागू होती है। सेवा का उपयोग जारी रखने वाले उपयोगकर्ता अप्रत्यक्ष रूप से अद्यतन शर्तों से सहमत होते हैं। किसी भी उल्लंघन के परिणामस्वरूप पहुंच निलंबित या समाप्त की जा सकती है।

Notes - Mistranslated "Dkt. 298" to "(डॉक्टर संख्या 298)" - interpreted as "Doctor number" instead of preserving the docket reference. No paragraph breaks.

Response - JigsawStack

इस न्यायालय ने पहले ही मस्क से उसके अनुचित समृद्धि और अनुबंध का उल्लंघन दावे के बीच चुनाव करने की आवश्यकता रखी थी। Dkt. 298। अब OpenAI भी मस्क से उसके अनुचित समृद्धि और टॉर्ट दावों के बीच चुनाव करने का मांग कर रहा है। यह मांग बेबुनियाद है। अनुचित समृद्धि और टॉर्ट दावों के तत्व भिन्न हैं, और मस्क दोनों सिद्धांतों को परीक्षण में जूरी के समक्ष प्रस्तुत कर सकता है। जहाँ विभिन्न दावों के तत्व भिन्न होते हैं, वहाँ वादी दोनों दावे जूरी के समक्ष प्रस्तुत कर सकता है, और जूरी फिर निर्णय कर सकती है कि कौन सा दावा या कौन से दावे साक्ष्य द्वारा समर्थित हैं। कोई न्यायालय वादी पर उन कारण-ए-कर्मों के बीच चुनाव थोप नहीं सकता जिन्हें वादी दलील करने का अधिकार रखता है। प्रणाली तीन चरणों में काम करती है। पहले, वितरित नोड्स से डेटा एकत्र किया जाता है। दूसरे, डेटा को मान्य और सामान्यीकृत किया जाता है। अन्ततः, संसाधित डेटा का सूचीकरण कर संग्रहीत किया जाता है। दूसरे चरण में विफलता तीसरे चरण में प्रगति को रोक देती है। यह नीति उन सभी उपयोगकर्ताओं पर लागू होती है जो 1 जनवरी के बाद प्लेटफ़ॉर्म का उपयोग करते हैं। जो उपयोगकर्ता सेवा का उपयोग जारी रखते हैं, वे निहित रूप से अद्यतित शर्तों से सहमत होते हैं। किसी भी उल्लंघन पर पहुँच निलंबन या समाप्ति हो सकती है।

Category 2: Grammar & Ambiguity

To evaluate this category, we take the following English source text and analyse its translation into Hindi, a language written in a non-Latin script.

Input Text -

The stack of documents that the intern who the manager mentored prepared is on the conference table, ready for the executive to review with the consultant who disagreed.

Expected Text -

दस्तावेज़ों का वह ढेर, जिसे उस इंटर्न ने तैयार किया जिसे प्रबंधक ने मार्गदर्शन दिया था, कॉन्फ्रेंस टेबल पर रखा है, कार्यकारी द्वारा उस सलाहकार के साथ समीक्षा के लिए तैयार जिसने असहमति जताई थी।

Response - ChatGPT Translate

दस्तावेज़ों का वह ढेर, जिसे उस इंटर्न ने तैयार किया जिसे प्रबंधक ने मार्गदर्शन दिया था, सम्मेलन कक्ष की मेज़ पर है, कार्यकारी द्वारा उस सलाहकार के साथ समीक्षा के लिए तैयार है जिसने असहमति जताई थी।

Response - Translate Gemma 27b

प्रबंधक द्वारा मार्गदर्शन किए गए इंटर्न द्वारा तैयार किए गए दस्तावेजों का ढेर सम्मेलन कक्ष की मेज पर रखा है, और यह उस सलाहकार के साथ समीक्षा के लिए तैयार है, जिसने इस पर असहमति जताई थी।

Response - Google Translation API

प्रबंधक के मार्गदर्शन में प्रशिक्षण प्राप्त कर रहे प्रशिक्षु द्वारा तैयार किए गए दस्तावेजों का ढेर कॉन्फ्रेंस टेबल पर रखा है, ताकि कार्यकारी अधिकारी असहमति जताने वाले सलाहकार के साथ उसकी समीक्षा कर सकें।

Notes - Google Translation API’s Hindi hides the chain

"प्रबंधक के मार्गदर्शन में प्रशिक्षण प्राप्त कर रहे प्रशिक्षु द्वारा तैयार किए गए दस्तावेजों का ढेर"

This reads like: "The stack of documents prepared by the trainee receiving training under the manager's guidance." Google removed the linking words and squashed the sentence into one long adjective phrase, also changing past action ("mentored") to present ("receiving training").

Response - JigsawStack

जिस इंटर्न को मैनेजर ने मेंटर किया था, उसने तैयार किए हुए दस्तावेज़ों का ढेर कांफ्रेंस टेबल पर है, कार्यकारी द्वारा उस कंसल्टेंट के साथ समीक्षा के लिए तैयार है जो असहमत था।

Category 3: Numbers, Dates, Identifiers & Entity

To evaluate this category, we take the following English source text and analyse its translation into Hindi, a language written in a non-Latin script.

Input Text -

Apple's Q2 report revealed sales of 3.5 million iPhone 15 Pro Max units at €1,299 each, while Samsung's $12.04 billion revenue in 2023 included devices shipping with USB-C, and the firmware update released on 03/04/2023 carried build ID a9f3c2e-rc.7.

Expected Text -

Apple की Q2 रिपोर्ट में 3.5 मिलियन iPhone 15 Pro Max यूनिट्स की बिक्री €1,299 प्रति यूनिट की दर से सामने आई, जबकि Samsung की 2023 में $12.04 बिलियन की आय में USB-C के साथ शिप होने वाले डिवाइस शामिल थे, और 03/04/2023 को जारी फ़र्मवेयर अपडेट का build ID a9f3c2e-rc.7 था।

Response - ChatGPT Translate

Apple की Q2 रिपोर्ट में €1,299 प्रति यूनिट की दर से 3.5 मिलियन iPhone 15 Pro Max यूनिट्स की बिक्री का खुलासा हुआ, जबकि 2023 में Samsung का $12.04 बिलियन का राजस्व USB-C के साथ भेजे गए उपकरणों को शामिल करता था, और 03/04/2023 को जारी किए गए फ़र्मवेयर अपडेट में बिल्ड आईडी a9f3c2e-rc.7 थी।

Response - Translate Gemma 27b

एप्पल की दूसरी तिमाही की रिपोर्ट में पता चला कि आईफोन 15 प्रो मैक्स के 3.5 मिलियन यूनिट 1,299 यूरो प्रति यूनिट की दर से बेचे गए, जबकि सैमसंग का 2023 में 12.04 बिलियन डॉलर का राजस्व, जिसमें यूएसबी-सी वाले उपकरणों की बिक्री शामिल थी, और 03/04/2023 को जारी किए गए फर्मवेयर अपडेट में बिल्ड आईडी a9f3c2e-rc.7 शामिल था।

Notes - Transliterated brand names to Hindi: "एप्पल", "आईफोन", "सैमसंग", "यूएसबी-सी" instead of keepin, replaced currency symbols: €1,299 → "1,299 यूरो", $12.04 billion → "12.04 बिलियन डॉलर".

Response - Google Translation API

एप्पल की दूसरी तिमाही की रिपोर्ट में €1,299 प्रति यूनिट की दर से 3.5 मिलियन आईफोन 15 प्रो मैक्स यूनिट की बिक्री का खुलासा हुआ, जबकि सैमसंग के 2023 में $12.04 बिलियन के राजस्व में यूएसबी-सी के साथ शिप किए गए डिवाइस शामिल थे, और 03/04/2023 को जारी किए गए फर्मवेयर अपडेट में बिल्ड आईडी a9f3c2e-rc.7 थी

Notes - Transliterated brand names to Hindi: "एप्पल", "आईफोन", "सैमसंग", "यूएसबी-सी" instead of keepin

Response - JigsawStack

Apple की Q2 रिपोर्ट ने बताया कि 3.5 मिलियन iPhone 15 Pro Max यूनिट्स की बिक्री हुई, प्रत्येक €1,299 में, जबकि Samsung की $12.04 बिलियन राजस्व 2023 में उन डिवाइसों को शामिल करती है जिनमें USB-C शिप हुआ, और 03/04/2023 को जारी किया गया फ़र्मवेयर अपडेट बिल्ड ID a9f3c2e-rc.7 लेकर आया।

Category 4: RTL and LTR Languages

To evaluate this category, we take the following English source text and analyse its translation into Arabic, a language written in a non-Latin script.

Input Text -

The meeting scheduled for 10 AM will review the file version (v2.1) that user @john_doe uploaded as report_v2.1.pdf at 9:45 PM, following Apple's launch of iOS 17.3 on 21/01/2024 in the UAE.

Expected Text -

الاجتماع المُجدوَل في الساعة 10 صباحًا سيُراجع إصدار الملف (v2.1) الذي رفعه المستخدم @john_doe باسم report_v2.1.pdf في الساعة 9:45 مساءً، بعد إطلاق Apple لنظام iOS 17.3 بتاريخ 21/01/2024 في الإمارات.

Response - ChatGPT Translate

سيُراجع الاجتماع المُجدول الساعة 10 صباحًا إصدار الملف (v2.1) الذي قام المستخدم @john_doe برفعه باسم report_v2.1.pdf عند الساعة 9:45 مساءً، وذلك عقب إطلاق آبل لنظام iOS 17.3 بتاريخ 21/01/2024 في دولة الإمارات العربية المتحدة.

Notes -Transliterated Apple → "آبل".

Response - Translate Gemma 27b

الاجتماع المقرر عقده في الساعة 10 صباحًا سيراجع النسخة النهائية من الملف (v2.1) التي قام المستخدم @john_doe بتحميلها تحت اسم report_v2.1.pdf في الساعة 9:45 مساءً، وذلك بعد إطلاق شركة آبل نظام التشغيل iOS 17.3 في 21 يناير 2024 في دولة الإمارات العربية المتحدة

Notes - Transliterated Apple → "آبل", Hallucination: Added "النسخة النهائية" (final version) - not in source, changed date format 21/01/2024→21 ي

Response - Google Translation API

سيستعرض الاجتماع المقرر عقده في الساعة العاشرة صباحاً نسخة الملف (v2.1) التي قام المستخدم @john_doe بتحميلها باسم report_v2.1.pdf في الساعة 9:45 مساءً، وذلك بعد إطلاق شركة آبل لنظام التشغيل iOS 17.3 في 21/01/2024 في الإمارات العربية المتحدة.

Notes - Wrote time in words "الساعة العاشرة صباحاً" instead of "10". Transliterated Apple → "آبل".

Response - JigsawStack

الاجتماع المقرر في الساعة 10:00 صباحًا سيستعرض إصدار الملف (v2.1) الذي حمّله المستخدم @john_doe كـ report_v2.1.pdf في الساعة 9:45 مساءً، وذلك بعد إطلاق Apple لنظام iOS 17.3 في 21/01/2024 في الإمارات.

Category 5: Slangs

To test this category, we take the following English source text and analyse its translation into Hindi, a language written in a non-Latin script.

Input Text -

Bro that shits kind of fire, have you tried out interfaze ? no cap bro, its a 10 on 10 latina baddie type vibe

Expected Text -

भाई वो चीज़ वाकई ज़बरदस्त है, क्या तूने interfaze आज़माया? सच्ची बात भाई, ये तो पूरे 10 में 10, लैटिना बैडी टाइप वाइब है।

Response - ChatGPT Translate

भाई, वो तो काफ़ी फायर है, क्या तूने interfaze ट्राय किया है? नो कैप भाई, ये पूरे 10 में 10, लैटिना बैडी टाइप वाइब है।

Response - Translate Gemma 27b

भाई, यह गाना वाकई कमाल का है, क्या तुमने इंटरफ़ेज़ सुना है? सच बता रहा हूँ, यह 10 में से 10 रेटिंग देने लायक, शानदार लैटिन संगीत है

Notes - Hallicunated "गाना" (song) and "संगीत" (music), Changed "tried out interfaze" → "इंटरफ़ेज़ सुना"(heard interfaze), “लैटिन संगीत" (Latin music) instead of "लैटिना बैडी" - completely wrong meaning

Response - Google Translation API

भाई, ये तो कमाल का है, क्या तुमने इंटरफ़ेज़ को आज़माया है? सच में भाई, ये एकदम लैटिना बैडी टाइप का है।

Notes - Doesn’t include “10 on 10” due to overformalizing

Response - JigsawStack

भाई, वो चीज़ वाकई ज़बरदस्त है, क्या तुमने interfaze आज़माया है? सच्ची बात कहूँ भाई, ये पूरी तरह से 10 में 10 लैटिना बड्डी वाला वाइब है

Run the Tests Yourself

All tests are fully reproducible. This repository contains the complete evaluation framework, including the evals.json file used to define and execute category-level assessments.

Conclusion

Our diagnostic evaluation across five translation categories reveals clear patterns in how different systems handle real-world translation challenges.

Translation Quality

JigsawStack emerged as the most reliable system for preserving meaning, structure, and fidelity across all test categories. Both successfully handled:

• Complex legal text with document identifiers

• Nested grammatical structures without simplification

• Exact preservation of numbers, dates, currencies, and version strings

• Informal slang without over-formalisation

Google Translate struggled with domain-specific content, mistranslating "Dkt. 298" as "Doctor number 298" and flattening nested clause structures. Gemma showed the most inconsistencies, including hallucinations in RTL text and currency symbol replacements.

Developer Experience

For production deployments, JigsawStack offers the best balance of quality and accessibility:

The Bottom Line

If you need accurate, structure-preserving translation with an easy-to-integrate API, JigsawStack delivers ChatGPT-level quality at 3x the rate limits of Google Translate, without the hallucination issues seen in open-source alternatives.

Comparison Table

◐ = partial | ❌ = inaccurate/fails | ✅ = accurate/succeeds

👥 Join the JigsawStack Community

Have questions or want to show off what you’ve built? Join the JigsawStack developer community on Discord and X/Twitter. Let’s build something amazing together!

We are also excited to enhance our capabilities. We have just launched a new version of our embedding model, offering much richer representations for different types of content and input languages.

Why a New Embedding Model?

Our first-generation embedding model (launched last year) introduced a unified vector space for text, images, PDFs, and audio in over 80 languages. It was built to address a key gap: most embeddings were text-only or English-only, making cross-modal, multilingual retrieval difficult. By supporting diverse formats with a single 768-dimensional representation, our v1 model enabled applications such as document and audio content retrieval, addressing the gap with a single query.

What changed?

Since then, the embedding landscape has evolved with larger models, and new techniques offer even better semantic capture and flexibility. With our latest model, you get:

• Richer High-Dimensional Vectors: Embedding v2 now outputs 4096-dimensional vectors (up from 768). This results in more accurate similarity searches and clustering, providing more relevant search results and better representation for data.

• Enhanced Multilingual & Cross-Modal Support: Our new model supports over 100+ languages and a variety of programming languages (up from 80). This means a single query vector can find matches in any language, e.g., a French audio clip could retrieve a relevant English paragraph.

• Instruction-Aware Customization: Embedding v2 is instruction-tuned, allowing it to better follow natural language guidance when generating embeddings. Greater customizability means you get embeddings aligned to your NLP task.

• Speaker Fingerprinting for Audio: You can now produce a unique speaker identity embedding from audio (The speaker fingerprint captures the speaker's voice traits, helping to identify or group speakers across clips).

Getting Started with Embedding v2

Embedding v2 is available now as part of our AI SDKs. It’s a drop-in upgrade; simply call the new /v2/embedding endpoint in our SDKs, and you’ll immediately start receiving the richer 4096-dim embeddings.

With Python:

#pip install jigsawstack or uv add jigsawstack
from jigsawstack import JigsawStack

jigsaw = JigsawStack(api_key="your-api-key")

response = jigsaw.embedding_v2({
  "url": "https://jigsawstack.com/preview/stt-example.wav",
  "type": "audio",
  "speaker_fingerprint": True
})

With JavaScript:

//npm install jigsawstack
import { JigsawStack } from "jigsawstack";

const jigsaw = JigsawStack({ apiKey: "your-api-key" });

const response = await jigsaw.embedding_v2({
  "url": "https://jigsawstack.com/preview/stt-example.wav",
  "type": "audio",
  "speaker_fingerprint": true
})

We’ve ensured compatibility with popular vector databases and libraries, so you can index the following embeddings with Faiss, Pinecone, or others with ease

{
  "success": true,
  "embeddings": [
        [
              0.01994265988469124,
             -0.00740474509075284,
              0.008083267137408257,
              ....
        ],
  "speaker_fingerprint": [
              0.01240834966301918,
             -0.016331281512975693,
             -0.009885231032967567,
              ....
        ],
  "_usage": {
        "input_tokens": 26,
        "output_tokens": 13239,
        "inference_time_tokens": 3253,
        "total_tokens": 16518
  }
}

Be sure to check out our embedding v2 documentation for getting started with your choice of programming language and options for your use case. With this upgrade, our model equips you with state-of-the-art embeddings, so you can build AI applications that truly understand your data in all its forms.

Embedding v2 in Action

Let us ship a real-world product: An audio app powered by Embedding v2, with features:

• Lyric/quote search → return artist, song title, and exact timestamp.

• Voice match (“more from this voice”) via speaker fingerprints across tracks.

• Natural-language Q&A over your song/podcast library with timestamp citations.

• Batch ingest + retrieval for multiple audio files; ready for FAISS/Pinecone/Qdrant.

As shown in the sequence diagram above, we transcribe with timestamps, embed every lyric segment, and store a speaker fingerprint per track. Ask “who said guddi sikhran di jatt ni?” and you’ll get: Shubh, “Elevated” [00:18–00:24], plus an option to “search more songs by this voice.” The same pattern works for podcasts, meetings, and call logs while using Embedding v2.

You ingest a list of songs from various artists, say, Shubh (Punjabi, pa), Miki Matsubara (Japanese, ja), Adila Sedraïa, known professionally as Indila (French, fr), & Dhanda Nyoliwala (Haryanvi, bgc).

We will consider 4 distinct singles (audio clips) for this MVP, one from each artist named above:

• Elevated by Shubh (lyrics)

• 真夜中のドア/Stay With Me by Miki Matsubara (lyrics)

• Dernière Danse by Indila (lyrics)

• Russian Bandana by Dhanda Nyoliwala (lyrics)

We make the following requests in English to perform cross-lingual retrieval:

1. Dhanda says he’s educated and must not be taken lightly.

curl -X POST "http://localhost:8000/who-said" \
-H "Content-Type: application/json" \
-d '{"quote":" Who sang this? Chhora Educate Laiye Na Gawara Mein Te (I’m educated, not someone to be taken lightly.) Nahi Mile Mere Jisse Re Hazara Mein Te (You won’t find someone like me, even among thousands.)"}'

Result:

{
  "answer": "dhanda said it in “russian bandana” [151.38s–154.27s]. I can search more songs by this voice if you want.",
  "best": {
    "artist": "dhanda",
    "title": "russian bandana",
    "timestamp": [
      151.38,
      154.27
    ],
    "track_id": "dhanda:russian bandana:156027077",
    "source_url": "<temp_url_generated>",
    "match_score": 0.561,
    "snippet": "ਛੋਰਾ ਏਜੁਕੇਟ ਲਾ ਯੇ ਨਾ ਗਵਾਰਾ ਮੈਤੇ ਨੈ"
  },
  "more_by_voice": []
}

2. Indila says, “I dance with the wind and rain“

curl -X POST "http://localhost:8000/who-said" \
-H "Content-Type: application/json" \
-d '{"quote":"Which artist says? I dance with the wind the rain"}'

Result

{
  "answer": "indila said it in “danse” [156.6s–159.58s]. I can search more songs by this voice if you want.",
  "best": {
    "artist": "indila",
    "title": "danse",
    "timestamp": [
      156.6,
      159.58
    ],
    "track_id": "indila:danse:89220676",
    "source_url": "<temp_url_generated>",
    "match_score": 0.6298,
    "snippet": "je danse avec"
  },
  "more_by_voice": []
}

3. Miki Matsubara refers to a person wearing a gray jacket with a coffee stain.

curl -X POST "http://localhost:8000/who-said" \
-H "Content-Type: application/json" \
-d '{"quote":"Which song has a reference to a gray jacket with coffee stain?"}'

Result

{
  "answer": "miki said it in “stay with me - mayonakara” [43.75s–46.65s]. I can search more songs by this voice if you want.",
  "best": {
    "artist": "miki",
    "title": "stay with me - mayonakara",
    "timestamp": [
      43.75,
      46.65
    ],
    "track_id": "miki:stay with me - mayonakara:230172378",
    "source_url": "<temp_url_generated>",
    "match_score": 0.5551,
    "snippet": "あなたはyouがそんな気もするグレイのジャケットに"
  },
  "more_by_voice": []
}

As we can see, English queries correctly matched lyrics in Punjabi (Gurmukhi), French, and Japanese, showing that our model can power retrieval by meaning, and not language or script, which is a huge win when dealing with multilingual data. The same pipeline works for podcasts, meetings, and call logs, multilingual queries over mixed audio libraries, with grounded answers and timestamp citations.

More importantly, you can replicate the example for PDFs, Images, and long texts with ease!

👥 Join the JigsawStack Community

Have questions or want to show off what you’ve built? Join the JigsawStack developer community on Discord and X/Twitter. Let’s build something amazing together!

At JigsawStack, we treat classification as a language task in addition to open vocabulary classification. It accepts text or images, arbitrary label strings, including long and descriptive ones, built on top of Small Vision Language Models; the result is a practical zero-shot classification that works, from multilingual support tickets to real-world images without retraining.

Where It Slaps 🔥

1) Crosslingual Text Classification

//npm install jigsawstack
import { JigsawStack } from "jigsawstack";

const jigsaw = JigsawStack({ apiKey: "your-api-key" });

const response = await jigsaw.classification({
  "dataset": [
        {
              "type": "text",
              "value": "Necesito un reembolso; me cobraron dos veces."
        },
        {
              "type": "text",
              "value": "सबमिट बटन काम नहीं कर रहा है।"
        }
  ],
  "labels": [
        {
              "type": "text",
              "value": "billing issue"
        },
        {
              "type": "text",
              "value": "feature request"
        },
        {
              "type": "text",
              "value": "bug"
        }
  ]
})

Result

{
    "success": true,
    "predictions": [
        "billing issue",
        "bug"
    ],
    "_usage": {
        "input_tokens": 145,
        "output_tokens": 14,
        "inference_time_tokens": 1386,
        "total_tokens": 1545
    }
}

2) Moderation & Intent Classification

const response = await jigsaw.classification({
    "dataset": [
        {
            "type": "text",
            "value": "sydney sweeney is mid"
        },
        {
            "type": "text",
            "value": "I'm thrilled to announce that I am part of JigsawStack"
        }
    ],
    "labels": [
        {
            "type": "text",
            "value": "hate"
        },
        {
            "type": "text",
            "value": "harrasment"
        },
        {
            "type": "text",
            "value": "spam"
        }
    ]
})

Result

{
    "success": true,
    "predictions": [
        "hate",
        "spam"
    ],
    "_usage": {
        "input_tokens": 127,
        "output_tokens": 12,
        "inference_time_tokens": 995,
        "total_tokens": 1134
    }
}

3) Tagging (Multi-label) Classification

const response = await jigsaw.classification({
    "dataset": [
        {
            "type": "text",
            "value": "I bought the AMD Ryzen 7 5700G to build a PC that could handle daily tasks, work, light gaming, and even some editing all without needing a dedicated graphics card right away. And honestly, I’m really impressed with how it performs. Right out of the box, this processor feels fast, stable, and efficient. With 8 cores and 16 threads, it handles multitasking like a champ. I can run multiple apps, edit videos, use development tools, and stream without lag or freezing. Everything is smooth and responsive."
        }
    ],
    "labels": [
        {
            "type": "text",
            "value": "hardware"
        },
        {
            "type": "text",
            "value": "powered by AMD"
        },
        {
            "key": "product review",
            "type": "text",
            "value": "This is a review from a user which talks about their recent computer hardware purchase."
        }
    ],
    "multiple_labels": true
})

Result

{
    "success": true,
    "predictions": [
        [
            "hardware",
            "powered by AMD",
            "product review"
        ]
    ],
    "_usage": {
        "input_tokens": 256,
        "output_tokens": 20,
        "inference_time_tokens": 1243,
        "total_tokens": 1519
    }
}

4) IRL/Context Aware Image Classification

Input image:

Classification:

const response = await jigsaw.classification({
    "dataset": [
        {
            "type": "image",
            "value": "https://images.unsplash.com/photo-1580655653885-65763b2597d0?q=80&w=1740&auto=format&fit=crop&ixlib=rb-4.1.0&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D"
        }
    ],
    "labels": [
        {
            "type": "text",
            "value": "This image is likely taken on the west coast of US"
        },
        {
            "type": "text",
            "value": "यह अमेरिका का हॉलीवुड है"
        }
    ],
    "multiple_labels": true
})

Result

{
    "success": true,
    "predictions": [
        [
            "This image is likely taken on the west coast of US",
            "यह अमेरिका का हॉलीवुड है"
        ]
    ],
    "_usage": {
        "input_tokens": 145,
        "output_tokens": 39,
        "inference_time_tokens": 4235,
        "total_tokens": 4419
    }
}

5) Image Classification with Image as a Label Reference

Input Image:

Reference images as labels:

The two reference images above (side-by-side), which are passed as input, showcase two hands in poker; the input image is of a hand where the player has a ‘Royal Flush‘, which is the best and rarest hand in poker, consisting of the ace, king, queen, jack, and ten of the same suit. The current task is to classify the hand that the player has based on the reference images as follows

const response = await jigsaw.classification({
    "dataset": [
        {
            "type": "image",
            "value": "https://images.unsplash.com/photo-1655008109440-df3a58567cee?q=80&w=2726&auto=format&fit=crop&ixlib=rb-4.1.0&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D"
        }
    ],
    "labels": [
        {
            "key": "This is a straight", // image on the left
            "type": "image",
            "value": "https://plus.unsplash.com/premium_photo-1694781503979-f2c7f599d269?q=80&w=870&auto=format&fit=crop&ixlib=rb-4.1.0&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D"
        },
        {
            "key": "This is a royal flush", //image on the right
            "type": "image",
            "value": "https://plus.unsplash.com/premium_photo-1671683370315-87306b0faf90?q=80&w=1744&auto=format&fit=crop&ixlib=rb-4.1.0&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D"
        }
    ]
})

Result

{
    "success": true,
    "predictions": [
        "This is a royal flush"
    ],
    "_usage": {
        "input_tokens": 217,
        "output_tokens": 14,
        "inference_time_tokens": 5748,
        "total_tokens": 5979
    }
}

Not Just Novel, but Smart

• Open-set safety: You can pass a label “unknown/none of the above” instead of forcing a bad match to avoid false positives.

• Label agility: Built with care for real-world generalization.

• Multilingual by construction: prompts and labels can be in the user’s language; we support mixed scripts and cross-lingual matching.

Zero-shot multimodal classification isn’t magic or a feat that can be achieved by training all the labels in the world; rather, it’s language-conditioned decision-making with honest uncertainty. By treating labels as language and leveraging SLMs, we get a classifier that adapts for better generalization without a retraining cycle.

👥 Join the JigsawStack Community

Have questions or want to show off what you’ve built? Join the JigsawStack developer community on Discord and X/Twitter. Let’s build something amazing together!

If prompt engineering was about cleverly wording a single query to coax a good answer, context engineering is about orchestrating everything the AI needs around that query. Ideally, it allows us to address the shortcomings of prompt engineering for tasks that require multi-step reasoning or developing answers scattered within various knowledge bases.

From Prompt Engineering to Context Engineering

Prompts by themselves couldn’t provide memory of past interactions implicitly, up-to-date knowledge, or ensure the AI stayed on track over a long dialogue. Developers realized that many failures of LLMs (like irrelevant or made-up answers) weren’t due to the model being “bad” – it was because the system around the model didn’t give it the right context or support. In other words, a clever prompt means little if it’s buried in a sea of irrelevant text or if the model lacks crucial information.

This insight gave rise to context engineering. Instead of focusing only on the prompt itself, context engineering is about designing the entire environment in which the AI operates. One article succinctly defines it as “structuring everything an LLM needs to complete a task successfully.” As Andrej Karpathy described, if prompt engineering is like writing a single sentence, context engineering is like writing an entire screenplay for the AI. You’re not just giving an instruction, you’re providing background, setting the scene, and making sure the AI has all the pieces to “get it right.”

Building Blocks of Context Engineering

So, what exactly goes into the engineering context for a language model? It typically means managing and curating a variety of inputs around the prompt, such as:

1. Conversation history and memory: Supplying relevant parts of prior chats or interactions so the model “remembers” important details.

2. Relevant documents or data: Fetching knowledge from databases, files, or the web (often via retrieval techniques) to inform the answer.

3. Structured instructions and context format: Framing information in a model-friendly way – for example, providing clear system instructions, or formatting data as tables or JSON if needed. Tools and actions: Allowing the model to invoke external tools or APIs (like calculators, web search, or code execution) and feeding those results into the context

4. Tools and actions: Allowing the model to invoke external tools or APIs (like calculators, web search, or code execution) and feeding those results into the context.

In essence, context engineering is about filling the model’s context window (its working memory) with the right information, at the right time, in the right format. A lot of devs would argue that they’ve been doing this since day one, and now we’ve just labeled it.

Why Context Engineering is the Latest Trend?

The move toward context engineering comes from practical necessity. As developers built more “industrial-strength” LLM applications. Context engineering delivers that by reducing guesswork. Instead of hoping a single prompt will make a model magically know about your 100-page knowledge base, you feed the knowledge base (or its highlights) into the model’s context.

Instead of the model forgetting what was said 10 messages ago, you make sure to persist important history forward. Rather than just asking the LLM to perform a task blindly, you might give it step-by-step context or even let it call functions and then supply the results back into the conversation.

Key factors that are driving this trend:

1. New tooling libraries and frameworks, pushing what’s possible in agents’ memory.

2. Growing context windows allow devs to feed whole documents whenever needed, thus achieving a better utilization of the language model’s context window.

3. It’s a system, not a sentence! A well-engineered context means devs don’t have to tailor prompts for every scenario.

Context engineering treats AI behavior as a systems design problem. Rather than solely relying on clever wording, it emphasizes architecting the information flow around the model.

What Do the Numbers Say About Context Engineering?

There is growing evidence that better results and stability can be achieved by setting the context for a language model, in contrast to a simple prompt.

A Databricks study evaluated retrieval‐augmented generation (RAG) using long‑context models, such as GPT-4‑turbo (128k tokens) and Claude 2 (200k tokens). They found that providing more retrieved documents increases answer quality, especially on QA tasks across corporate and financial domains.

However, models struggle if the context is too long (“lost in the middle”), so properly chunking and curating context is critical [1].

Thomson Reuters benchmarked long‑context LLMs (up to 1 M tokens) vs retrieval-based. Their internal tests revealed that feeding full legal documents into the LLM outperformed RAG in most document-based reasoning tasks. LC models handled multi-hop reasoning more effectively than RAG approaches [2].

Additionally, research from Nvidia presented at ICLR 2025 showed:

• Best results come from combining retrieval and long-context capabilities.

• A LLaMA2‑70B with 32k context + retrieval matched or surpassed GPT‑3.5‑turbo‑16k and Davinci003.

• The LC+RAG combo outperformed LC-only baselines, showing clear synergy [3].

Final Takeaway

With growing excitement around context setting (engineering), and quantitative proofs backing this excitement within the community, it proves that it isn’t hype but is empirically superior for domain-specific knowledge work.
Language Models with large context windows shine when their context window utilization is higher, with targeted retrieval, where dump=everything strategies falter. Thus, backing the sentiment that feeding the model the right context, even via retrieval pipelines or using very long context data inputs, yields measurable gains over prompt-only methods.

It is about building a system, not a prompt.

References

1. Leng, Q., Portes, J., Havens, S., Zaharia, M. and Carbin, M. (2024) ‘Long Context RAG Performance of LLMs’. Databricks Blog, 12 August. Available at: https://www.databricks.com/blog/long-context-rag-performance-llms (Accessed: 12 July 2025).

2. Hron, J. (2025) ‘Legal AI Benchmarking: Evaluating Long Context Performance for LLMs’. Thomson Reuters Innovation Blog, 14 April. Available at: https://blogs.thomsonreuters.com/en-us/innovation/legal-ai-benchmarking-evaluating-long-context-performance-for-llms/ (Accessed: 12 July 2025).

3. Xu, P., Ping, W., Wu, X., McAfee, L., Zhu, C., Liu, Z., Subramanian, S., Bakhturina, E., Shoeybi, M. and Catanzaro, B. (2023) ‘Retrieval Meets Long Context Large Language Models’. arXiv preprint arXiv:2310.03025. Available at: https://arxiv.org/abs/2310.03025 (Accessed: 12 July 2025).

👥 Join the JigsawStack Community

Have questions or want to show off what you’ve built? Join the JigsawStack developer community on Discord and X/Twitter. Let’s build something amazing together!

Object detection is like giving machines eyes. It’s the computer vision task of finding and localizing people, cars, cups, and other objects in images or videos. Unlike plain image classification (which simply states “this is a cat”), object detection draws little boxes around objects.

At JigsawStack, we’ve been developing the best small object detection and grounding model available, built to handle not just detection, but also spatial grounding, segmentation, and even UI element detection for computer use. One model. All the sauce.

In a world where browser agents now see, click, and reason, object detection is the backbone behind not just smart cameras, but UI automation, accessibility tech, and even creative tools. Computer use itself has exploded in the last three years, and under the hood, it’s all powered by the same core: visual grounding for object detection.

Vision-Language Models in Detection

Historically, object detection has been specialized for specific industries, factories, construction sites, and even medical research. But at JigsawStack, we asked: Can we build a single, small, general-purpose model that handles everything from segmenting any real-world object to grounding UI buttons?

Early VLMs were used for classification or captioning, but now developers are adapting them to draw boxes. For example, CLIP-inspired methods (such as GLIP, Grounding DINO, and OWL-ViT) transform language supervision into object localization. These models let you pose queries like “Where is the apple?” and get back bounding boxes or masks. Grounding DINO and OWL-ViT are prime examples: they leverage text prompts to find novel objects at first attempt (zero-shot).

Here’s the state of object detection in 2025:

• You can now just type “find the red mug” and open-vocabulary detectors like Grounding DINO 1.5 will box it, even if “red mug” was never in the training set.

• Meta’s shiny SAM 2.1 goes beyond boxes by carving out pixel-perfect cut-outs of any object with one click or prompt.

• Chatty AIs such as GPT-4o can reply with bounding-box coordinates straight from plain-English questions.

• Edge-tuned versions like Grounding DINO 1.5 Edge now run on phones and drones, so object detection works offline in real time.

• Devs are chaining detectors to segmenters, think Grounded SAM 2 for one-click “find-and-mask” pipelines in production APIs.

Examples: What can our model do?

1. Basic Object Detection: detects all visible objects.

2. Prompt-based Object Detection: Find anything you can describe—“find the red mug,” “show me cranes,” or even “utensils which can be used for frying”. Take a look at what our model detects for the prompt “four-legged fren <3“

3. Computer Use: understands and localizes user interface elements (e.g., buttons, sliders) for computer use. Following is a screenshot for detections for VScode with a chat input pop-up for local computer use.

4. Browser Automation: Automate website navigation by GUI element grounding. With prompting e.g. “search field“, “give me the search button“, e.t.c.

Can JigsawStack see an image differently?

Totally! Because we built the model around phenomenon vision-language alignment instead of just cranking up the model size. Alignment means the pixel-side encoder and the text-side encoder learn to land in the same semantic space, so “oplayer” and the blob of pixels that is a player overlap on the inside. Do that well and you get:

• Generalisation on tap – snap to the right region even if they never showed up in training.

• Promptable precision – ask for a UI button, a road sign, or a corgi wearing goggles, and the model adaptively tightens its boxes or masks.

• One-shot spatial reasoning – verbs like left of, under, right, or next to that pillar come baked in, no extra heads required.

Small model, aligned brains, fresh set of eyes for every prompt. Let’s take a look to see if our model can point to the right objects ;)

Grid: Comparing Gemini-2.5-Pro (top-right), GPT-4.1 (bottom-left), & JigsawStack-ObjectDetection (bottom-right) on Minecraft (GUI-grounding), with prompt “player“

Grid: Comparing Gemini-2.5-Pro (top-right), GPT-4.1 (bottom-left), & JigsawStack-Object Detection (bottom-right) for object_detection with prompt “crane“

Let’s take a look at segmentation ability as well!

Image: JigsawStack-Object Detection for Segmentation, with prompt “helmets“

Image: fal-ai/SAM-evf for Segmentation, with prompt “helmets“

Quick Start with JigsawStack Object Detection

Getting started with our object detection model is incredibly straightforward. Whether you're developing a web or mobile app, our JavaScript and Python SDKs make the process seamless. With just a few lines of code, you can tap into JigsawStack's capabilities to detect, segment, and annotate objects in images, adding a layer of intelligence and interactivity to your application. Ready to dive in? Let's explore how you can set up and make your first call using our SDKs

Image: Golden Gate Bridge in San Francisco

Let’s start by setting up JigsawStack SDK,

npm install jigsawstack (or) pip install jigsawstack

Make your first call with ease through our JS and Python SDKs

import { JigsawStack } from "jigsawstack";

const jigsaw = JigsawStack({ apiKey: "your-api-key" });

const response = await jigsaw.vision.object_detection({
  "url": "https://blog.viajemos.com/wp-content/uploads/2024/06/Que-hacer-en-San-Francisco-3-dias-1-1.png",
  "annotated_image": true,
})

Similarly, in Python, you can use the object detection service as follows:

from jigsawstack import JigsawStack

jigsaw = JigsawStack(api_key="your-api-key")

response = jigsaw.vision.object_detection({
  "url": "https://blog.viajemos.com/wp-content/uploads/2024/06/Que-hacer-en-San-Francisco-3-dias-1-1.png",
  "annotated_image": true,
})

And voila!

{
    "success": true,
    "annotated_image": "https://jigsawstack-temp.b1e91a466694ad4af04df5d05ca12d93.r2.cloudflarestorage.com/temp/62818eaf-0573-45c1-ac55-601302e567ab.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=7b9a19349842b7b1a9e4c2e19f05b232%2F20250624%2Fauto%2Fs3%2Faws4_request&X-Amz-Date=20250624T172243Z&X-Amz-Expires=604800&X-Amz-Signature=8d5bdade297836deada8c33d81dd5d638c89a3a83014ffc18dc834c6939f4938&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject",
    "gui_elements": [],
    "objects": [
        {
            "bounds": {
                "top_left": {
                    "x": 0,
                    "y": 0
                },
                "top_right": {
                    "x": 1900,
                    "y": 0
                },
                "bottom_left": {
                    "x": 0,
                    "y": 728
                },
                "bottom_right": {
                    "x": 1900,
                    "y": 728
                },
                "width": 1900,
                "height": 728
            },
            "label": "Sky",
            "mask": "https://jigsawstack-temp.b1e91a466694ad4af04df5d05ca12d93.r2.cloudflarestorage.com/temp/37a2ad76-ab17-4ffc-98d9-58aad848d162.png?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=7b9a19349842b7b1a9e4c2e19f05b232%2F20250624%2Fauto%2Fs3%2Faws4_request&X-Amz-Date=20250624T172244Z&X-Amz-Expires=604800&X-Amz-Signature=d1da3b9fb1cd46cac0d5aa9d8178f970568a016abd6864c5cfa18257f3e4ba32&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject"
        },
        {
            "bounds": {
                "top_left": {
                    "x": 411,
                    "y": 143
                },
                "top_right": {
                    "x": 1900,
                    "y": 143
                },
                "bottom_left": {
                    "x": 411,
                    "y": 1066
                },
                "bottom_right": {
                    "x": 1900,
                    "y": 1066
                },
                "width": 1489,
                "height": 923
            },
            "label": "Golden Gate Bridge",
            "mask": "https://jigsawstack-temp.b1e91a466694ad4af04df5d05ca12d93.r2.cloudflarestorage.com/temp/dcc4363c-4239-47ab-a58c-48ad036deb43.png?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=7b9a19349842b7b1a9e4c2e19f05b232%2F20250624%2Fauto%2Fs3%2Faws4_request&X-Amz-Date=20250624T172244Z&X-Amz-Expires=604800&X-Amz-Signature=1b7587e4b39ec4640895dfffc54fb4ecea8c738ff19f6150c44c38bebaa35bd0&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject"
        },
        ...
    ],
    "_usage": {
        "input_tokens": 36,
        "output_tokens": 3185,
        "inference_time_tokens": 23758,
        "total_tokens": 26979
    }
}

Here's the annotated image showcasing our model's ability to detect, segment, label, and annotate.

Image**: Result from JigsawStack-Object Detection

Play With JigsawStack Object Detection

Users can also pass in natural language prompts to ground objects based on the intent & visual context in the scene.

Consider the following example:

from jigsawstack import JigsawStack

jigsaw = JigsawStack(api_key="your-api-key")

response = jigsaw.vision.object_detection({  
   "prompts": ["Objects that can be used to illuminate the room."],
   "url": "https://images.unsplash.com/photo-1607448421773-f74d4b9b5919?q=80&w=1754&auto=format&fit=crop&ixlib=rb-4.1.0&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D",
   "annotated_image": true
})

Our model detects:

Image**: Result from JigsawStack-Object Detection for user query “Objects that can be used to illuminate the room.“

Our approach to generalizing object detection and segmentation is transforming the way developers integrate visual intelligence into their applications. With our user-friendly SDKs and powerful vision-language alignment, you can effortlessly enhance your projects with cutting-edge technology. Whether you're working on a web or mobile app, JigsawStack provides the tools you need to bring your ideas to life. And we’re excited to see devs build with our smoll models capable of big impact!

👥 Join the JigsawStack Community

Have questions or want to show off what you’ve built? Join the JigsawStack developer community on Discord and X/Twitter. Let’s build something amazing together!

But as the web has changed, so have expectations, now we want translations that are more accurate, understands contexts of what’s trying to be said, easier to customize/control, and give developers more control.

Unfortunately the Google Translate Widget hasn’t been maintained for a long time and is now deprecated.

🌐 What made Google Translate Widget so Great

Google Translate widget made it super easy to translate websites:

• It supported 100+ languages out of the box

• Setup was simple, just drop in a script and you’re done

• It could automatically detect the user’s browser language

• You didn’t need to build any backend logic to make it work

We took all the great things of Google Translate Widget and added support for dynamic frontend sites built on frameworks like React/NextJS, improve the UI, adding more languages and made it even easier to use.

🚀 Introducing the JigsawStack Translation Widget

It’s built for modern websites and developers who care about speed, accuracy, and flexibility. Here’s what makes it stand out:

• ✅ Add it to any site with just one line.

• 📦 NPM package support for React, Next.js, and more.

• ⚡ Lightning-fast with smart caching built in.

• 🎨 Fully customizable UI that blends right into your design.

• 🔄 Handles dynamic websites and content updates seamlessly.

• 🧠 Built-in APIs so you can trigger translations manually when needed.

• 🧩 Works with everything from plain HTML pages to complex apps.

👉 Check out the live demo - https://translation-widget-demo.vercel.app

👉 Check out the github repository - https://github.com/jigsawStack/translation-widget

🛠️ How to Add It to Your Website

Adding the jigsaw translation widget is simple and easy, whether you are using plain HTML or Modern Frameworks like react or Next.js, you're covered.

🔧 Script Integration

If you’re working with a basic HTML site or just want to get started quickly, you can use the one-line script method:

1. Add the Widget Script Place this just before closing the tag of your HTML file

2. Initialize the Widget Immediately after the script, add this initialization code

    <script defer src="https://unpkg.com/translation-widget@latest/dist/index.min.js"></script>
    <script defer type="module">
    // Replace "YOUR_PUBLIC_KEY_HERE" with your public API key from JigsawStack's dashboard.
      TranslationWidget('YOUR_PUBLIC_KEY_HERE', {
        pageLanguage: 'en',         // Optional
        position: 'top-right',      // Optional
        autoDetectLanguage: false,  // Optional
        showUI: true,               // Optional
        theme: {
          baseColor: '',     // Optional
          textColor: '',     // Optional
        }
      });
    </script>
   

⚛️ Next.js / React Integration (NPM Way)

If you’re using a modern frontend stack, install the widget using npm:

npm install translation-widget
or
yarn add translation-widget
or
pnpm add translation-widget

📦 Usage with React / Next.js

1. Create a Translation Component Create a React component to initialize the widget:

    "use client";
    import { useEffect } from "react";
    import TranslationWidget from "translation-widget";
   
    export default function Translation() {
      useEffect(() => {
        // Replace "YOUR_PUBLIC_KEY_HERE" with your public API key from JigsawStack's dashboard.
        TranslationWidget("YOUR_PUBLIC_KEY_HERE", {
          showUI: true,
          pageLanguage: 'en',
          position: "top-right",
          autoDetectLanguage: false,
        });
      }, []);
   
      return null;
    }
   

2. Use it in your layout: import and include the component in your layout file or app.tsx, depending on the framework you are using.

    import Translation from './components/Translation';
   
    export default function Layout({ children }) {
      return (
        <>
          {children}
          <Translation />
        </>
      );
    }
   

👀 Translating Amazon.com with JigsawStack Translate Widget

🔁 Google Translate Widget vs JigsawStack Translate Widget

We took it as a challenge to do better, so here’s how we compare to the deprecated Google Translation Widget.

🧠 Want More Control? Use the Translation API Directly

If you’d want to handle translations yourself, for example to preprocess content on the server or integrate with your backend logic, you can use the JigsawStack Translation API directly.

This gives you:

• High quality translation with context understanding

• Full control over the translation lifecycle

• Ability to translate content before rendering

• Integration with CMS, database, or other services

👉 Check out the official API docs and dashboard here:
JigsawStack Translation API →

🚀 Final Thoughts

Google Translate Widget played a huge role in making the web more accessible across languages, but as websites evolved, so did the need for smarter, faster, and more customizable translation tools.

The JigsawStack Translation Widget brings translation into the modern era:
It’s lightweight, context-aware, developer-friendly, and built to handle today’s dynamic frontends.

Whether you're building a landing page, a global product, or a multilingual documentation site this widget gives you full control, without the bloat.

Ready to upgrade your translation experience?
👉 Try the live demo or check out the GitHub repo to get started.

JigsawStack’s Deep Research is an open source framework performing for multi-hop, AI-assisted research. The framework orchestrates large language models (LLMs), recursive web searches, and structured reasoning to generate comprehensive, evidence-backed reports via our deep-research SDK. Designed for developers, researchers, and AI agents. We aim to automate the kind of deep inquiry that typically takes hours or even days.

Get a quick start by trying out our SDK here: https://github.com/JigsawStack/deep-research

import { createDeepResearch } from "deep-research";

const deepResearch = createDeepResearch({
  OPENAI_API_KEY: process.env.OPENAI_API_KEY,
  GEMINI_API_KEY: process.env.GEMINI_API_KEY,
  DEEPINFRA_API_KEY: process.env.DEEPINFRA_API_KEY,
  JIGSAW_API_KEY: process.env.JIGSAW_API_KEY,
});

const result = await deepResearch.generate("What are the recent developments in quantum computing?");

console.log(result.data.text);           // Full research report
console.log(result.data.bibliography);   // Auto-generated references

Core Concepts

How JigsawStack Implements Deep Research

Deep Research performs the following:

1. Research Plan, decomposing complex query into logical subtopics and objectives.

2. AI Web Searches via JigsawStack API, generating context and extracting key points from the web

3. Multi-Model Reasoning and Decision Making, the framework adapts its strategy based on findings, filling knowledge gaps as needed

4. Report Generation, including structured content and source attribution for traceability

Fully Customizable

One of the core strengths of JigsawStack’s Deep Research is how deeply customizable it is.

• Precise Control Over Depth & Breadth

• Report Length Targeting - Configure your desired report length with soft and hard output limits

• Developer-Friendly Debugging - Enable detailed logging for developers

• Model Modularity - Mix and match AI Providers based on their strengths

const deepResearch = createDeepResearch({
  report: {
    targetOutputTokens: 10000,  // Target length for the generated report
    maxOutputTokens: 30000,     // Hard ceiling for total output
  },
  depth: { maxDepth: 4 },       // How many recursive research cycles to run
  breadth: { maxBreadth: 3 },   // How many subqueries to generate per level
  models: {
    default: openaiModel,       // Main LLM for prompt interpretation
    reasoning: deepseekModel,   // Specialized model for analyzing research findings
    output: geminiModel,        // Model used for final report generation
  },
  logging: { enabled: true },   // Enable verbose logging for debugging
});

Current Market and the Challenges of Deep Research

While many are pushing boundaries, current implementations face a few common challenges:

• Citation Accuracy & Transparency

• Limited Customization & Developer Control

• Closed-Source & Opaque Systems

• Limited Integration Options

We aim to address these pain points, making it a research platform you can build on.

Comparisons

Real World Examples

Write a research paper talking about diffusion for LLMs and how it might affect the future in technology?

const query = "Write a research paper talking about diffusion for LLMs and how it might affect the future in technology?";

const result = await deepResearch.generate(query);

console.log(result.data.text) // report
console.log(result.data.bibliography) // references

const singleReport = result.data.text + "\n\n" + result.data.bibliography

What is the largest order of a non-cyclic torsion subgroup of an elliptic curve over \mathbb{Q}(\sqrt{-3})\)

const query = "What is the largest order of a non-cyclic torsion subgroup of an elliptic curve over \mathbb{Q}(\sqrt{-3})";

const result = await deepResearch.generate(query);

console.log(result.data.text) // report
console.log(result.data.bibliography) // references

Improvements to JigsawStack’s Deep Research

While Deep Research provides powerful automation for complex inquiry, it’s not without its trade-offs and design challenges:

• Runtime Trade-offs - deep research deliberately shifts away from the fast-but-shallow answers towards deep and completed ones.

• Exclusive to Public Information - we are unable to access paywalled research papers, private documents, or internal knowledge bases

• Sources Discrepancy - Some sources provided are not quoted on the report. Even if a URL isn’t directly cited in the final report, it may still play a critical role in shaping the system’s reasoning.

• Evolving output format - The first stage of Deep Research prioritizes correctness, structure, and citations. But the ideal report format — tone, length, granularity — will vary by user and use case. We’re actively evolving the output schema based on real-world feedback to support!

Next Steps

As we continue to evolve Deep Research, the next major milestone is full integration into JigsawStack’s web search API.

Currently, Deep Research uses JigsawStack’s search API for retrieving public content. Going forward, we’re expanding this connection to make the research experience even more seamless and intelligent.

Before we jump into the tests we ran, we need to understand what makes a good web scraper or even what’s web scraping in the first place.

Traditionally, developers wrote large, complex codebases on top of heavy infrastructure to load and extract content from websites. They used browser engines like Puppeteer, Playwright, or Selenium, followed by CSS selectors and various techniques to get the data they needed.

Today, we have AI scrapers that do the same thing in seconds, fully managed and automated through simple prompting.

So what makes a good AI Web scraper?

• Extracts data with the accuracy of a developer-written script

• Automatically handles errors, browser crashes, popups, and other blockers

• Auto unblocker for websites protected by Cloudflare, Captcha, and more

• Fully managed scaling from 1 to 10,000 requests and back in seconds

• Smart actions such as auto-scrolling to load and extract dynamic content

• Rotates IPs intelligently to avoid being blocked or flagged as a bot

• Gives developers control with support for custom CSS selectors

Summary Comparison

Firecrawl vs JigsawStack AI Scraper

◐ = partial ❌ = inaccurate/fails ✅ = accurate/succeeds

Full Comparisons

Something simple

Site: https://news.ycombinator.com
Prompt: ["post_titles", "post_points", "post_username"]

Firecrawl

{
    "success": true,
    "metadata": {
      "viewport": "width=device-width, initial-scale=1.0",
      "favicon": "https://news.ycombinator.com/y18.svg",
      "title": "Hacker News",
      "referrer": "origin",
      "language": "en",
      "scrapeId": "686271b5-f619-417b-800a-f57b95236a4f",
      "sourceURL": "https://news.ycombinator.com/",
      "url": "https://news.ycombinator.com/",
      "statusCode": 200,
      "proxyUsed": "basic"
    },
    "json": {
      "post_titles": [
        "Reinvent the Wheel",
        "Tachy0n: The Last 0day Jailbreak",
        "I used o3 to find a remote zeroday in the Linux SMB implementation",
        "Why old games never die, but new ones do",
        "Using the Apple ][+ with the RetroTink-5X",
        "Lone coder cracks 50-year puzzle to find Boggle's top-scoring board",
        "Good Writing",
        "The Logistics of Road War in the Wasteland",
        "Show HN: Rotary Phone Dial Linux Kernel Driver",
        "Please Fund More Science (2020)",
        "The Xenon Death Flash: How a Camera Nearly Killed the Raspberry Pi 2",
        "Hong Kong's Famous Bamboo Scaffolding Hangs on (For Now)",
        "Exposed Industrial Control Systems and Honeypots in the Wild [pdf]",
        "Show HN: F2 – Cross-Platform CLI Batch Renaming Tool",
        "Postgres IDE in VS Code",
        "Scientific conferences are leaving the US amid border fears",
        "Trellis (YC W24) Is Hiring founding SDR to help automate healthcare paperwork",
        "An Almost Pointless Exercise in GPU Optimization",
        "Peer Programming with LLMs, for Senior+ Engineers",
        "Live facial recognition cameras may become 'commonplace' as police use soars",
        "Is Astrophotography Without Tracking Possible?",
        "The Last Nomads",
        "Show HN: 1 min workouts for people who sit all day",
        "The legacy of the iconic Nakagin capsule tower",
        "Find Your People",
        "AI, Heidegger, and Evangelion",
        "Root for your friends",
        "Why Algebraic Effects?",
        "voyage-3.5 and voyage-3.5-lite: improved quality for a new retrieval frontier",
        "Use ramoops for logging under Linux (2021)"
      ],
      "post_points": [
        "112",
        "104",
        "264",
        "26",
        "9",
        "69",
        "118",
        "22",
        "262",
        "51",
        "159",
        "134",
        "26",
        "88",
        "969",
        "18",
        "0",
        "5",
        "58",
        "101",
        "17",
        "5",
        "57",
        "81",
        "702",
        "103",
        "364",
        "267",
        "20",
        "12"
      ],
      "post_username": [
        "zdw",
        "todsacerdoti",
        "zielmicha",
        "airhangerf15",
        "zdw",
        "DavidSJ",
        "oli5679",
        "ecliptik",
        "sephalon",
        "ssuds",
        "DamonHD",
        "perihelions",
        "gnabgib",
        "ayoisaiah",
        "Dowwie",
        "mdhb",
        "null",
        "atomlib",
        "pmbanugo",
        "c-oreills",
        "astroimagery",
        "Thevet",
        "melvinzammit",
        "pseudolus",
        "jl",
        "jger15",
        "rez0123",
        "jiggawatts",
        "fzliu",
        "zdw"
      ]
    }
  }

JigsawStack

{
    "page_position": 1,
    "page_position_length": 1,
    "context": {
      "post_titles": [
        "Reinvent the Wheel",
        "Tachy0n: The Last 0day Jailbreak",
        "I used o3 to find a remote zeroday in the Linux SMB implementation",
        "Why old games never die, but new ones do",
        "Using the Apple ][+ with the RetroTink-5X",
        "Lone coder cracks 50-year puzzle to find Boggle's top-scoring board",
        "Good Writing",
        "The Logistics of Road War in the Wasteland",
        "Show HN: Rotary Phone Dial Linux Kernel Driver",
        "Please Fund More Science (2020)",
        "The Xenon Death Flash: How a Camera Nearly Killed the Raspberry Pi 2",
        "Hong Kong's Famous Bamboo Scaffolding Hangs on (For Now)",
        "Exposed Industrial Control Systems and Honeypots in the Wild [pdf]",
        "Show HN: F2 – Cross-Platform CLI Batch Renaming Tool",
        "Postgres IDE in VS Code",
        "Scientific conferences are leaving the US amid border fears",
        "Trellis (YC W24) Is Hiring founding SDR to help automate healthcare paperwork",
        "An Almost Pointless Exercise in GPU Optimization",
        "Peer Programming with LLMs, for Senior+ Engineers",
        "Live facial recognition cameras may become 'commonplace' as police use soars",
        "Is Astrophotography Without Tracking Possible?",
        "The Last Nomads",
        "Show HN: 1 min workouts for people who sit all day",
        "The legacy of the iconic Nakagin capsule tower",
        "Find Your People",
        "AI, Heidegger, and Evangelion",
        "Root for your friends",
        "Why Algebraic Effects?",
        "voyage-3.5 and voyage-3.5-lite: improved quality for a new retrieval frontier",
        "Use ramoops for logging under Linux (2021)",
        "endler.dev",
        "siguza.net",
        "heelan.io",
        "pleromanonx86.wordpress.com",
        "nicole.express",
        "ft.com",
        "paulgraham.com",
        "acoup.blog",
        "gitlab.com/sephalon",
        "samaltman.com",
        "magnus919.com",
        "nytimes.com",
        "gsmaragd.github.io",
        "github.com/ayoisaiah",
        "microsoft.com",
        "nature.com",
        "ycombinator.com",
        "speechmatics.com",
        "pmbanugo.me",
        "theguardian.com",
        "astroimagery.com",
        "thedial.world",
        "shortreps.com",
        "designboom.com",
        "foundersatwork.posthaven.com",
        "fakepixels.substack.com",
        "josephthacker.com",
        "antelang.org",
        "voyageai.com",
        "embear.ch"
      ],
      "post_points": [
        "112 points",
        "104 points",
        "264 points",
        "26 points",
        "9 points",
        "69 points",
        "118 points",
        "22 points",
        "262 points",
        "51 points",
        "159 points",
        "134 points",
        "88 points",
        "969 points",
        "18 points",
        "5 points",
        "58 points",
        "101 points",
        "17 points",
        "57 points",
        "81 points",
        "702 points",
        "103 points",
        "364 points",
        "267 points",
        "20 points",
        "12 points"
      ],
      "post_username": [
        "zdw",
        "todsacerdoti",
        "zielmicha",
        "airhangerf15",
        "DavidSJ",
        "oli5679",
        "ecliptik",
        "sephalon",
        "ssuds",
        "DamonHD",
        "perihelions",
        "gnabgib",
        "ayoisaiah",
        "Dowwie",
        "mdhb",
        "atomlib",
        "pmbanugo",
        "c-oreills",
        "astroimagery",
        "Thevet",
        "melvinzammit",
        "pseudolus",
        "jl",
        "jger15",
        "rez0123",
        "jiggawatts",
        "fzliu"
      ]
    },
    "selectors": {
      "post_titles": ["tr.athing .titleline > a", "span.titleline > a", "td.title .titleline > a", "tr.athing .titleline a", ".titleline > a"],
      "post_points": ["span.score", ".subtext .score", "td.subtext span.score", "span.subline > span.score", ".score"],
      "post_username": ["a.hnuser", ".subtext .hnuser", "span.subline > a.hnuser", "td.subtext a.hnuser", ".hnuser"]
    },
    "link": [
      {
        "href": "https://news.ycombinator.com",
        "text": "",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/news",
        "text": "Hacker News",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/newest",
        "text": "new",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/front",
        "text": "past",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/newcomments",
        "text": "comments",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/ask",
        "text": "ask",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/show",
        "text": "show",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/jobs",
        "text": "jobs",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/submit",
        "text": "submit",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/login?goto=news",
        "text": "login",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/vote?id=44083467&how=up&goto=news",
        "text": "",
        "type": "a"
      },
      {
        "href": "https://endler.dev/2025/reinvent-the-wheel/",
        "text": "Reinvent the Wheel",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/from?site=endler.dev",
        "text": "endler.dev",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/user?id=zdw",
        "text": "zdw",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44083467",
        "text": "2 hours ago",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/hide?id=44083467&goto=news",
        "text": "hide",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44083467",
        "text": "58 comments",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/vote?id=44083388&how=up&goto=news",
        "text": "",
        "type": "a"
      },
      {
        "href": "https://blog.siguza.net/tachy0n/",
        "text": "Tachy0n: The Last 0day Jailbreak",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/from?site=siguza.net",
        "text": "siguza.net",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/user?id=todsacerdoti",
        "text": "todsacerdoti",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44083388",
        "text": "3 hours ago",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/hide?id=44083388&goto=news",
        "text": "hide",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44083388",
        "text": "8 comments",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/vote?id=44081338&how=up&goto=news",
        "text": "",
        "type": "a"
      },
      {
        "href": "https://sean.heelan.io/2025/05/22/how-i-used-o3-to-find-cve-2025-37899-a-remote-zeroday-vulnerability-in-the-linux-kernels-smb-implementation/",
        "text": "I used o3 to find a remote zeroday in the Linux SMB implementation",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/from?site=heelan.io",
        "text": "heelan.io",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/user?id=zielmicha",
        "text": "zielmicha",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44081338",
        "text": "8 hours ago",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/hide?id=44081338&goto=news",
        "text": "hide",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44081338",
        "text": "77 comments",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/vote?id=44083917&how=up&goto=news",
        "text": "",
        "type": "a"
      },
      {
        "href": "https://pleromanonx86.wordpress.com/2025/05/06/why-old-games-never-die-but-new-ones-do/",
        "text": "Why old games never die, but new ones do",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/from?site=pleromanonx86.wordpress.com",
        "text": "pleromanonx86.wordpress.com",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/user?id=airhangerf15",
        "text": "airhangerf15",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44083917",
        "text": "1 hour ago",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/hide?id=44083917&goto=news",
        "text": "hide",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44083917",
        "text": "9 comments",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/vote?id=44083910&how=up&goto=news",
        "text": "",
        "type": "a"
      },
      {
        "href": "https://nicole.express/2025/apple-ii-more-like-apple-5x.html",
        "text": "Using the Apple ][+ with the RetroTink-5X",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/from?site=nicole.express",
        "text": "nicole.express",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/user?id=zdw",
        "text": "zdw",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44083910",
        "text": "1 hour ago",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/hide?id=44083910&goto=news",
        "text": "hide",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44083910",
        "text": "discuss",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/vote?id=44082892&how=up&goto=news",
        "text": "",
        "type": "a"
      },
      {
        "href": "https://www.ft.com/content/0ab64ced-1ed1-466d-acd3-78510d10c3a1",
        "text": "Lone coder cracks 50-year puzzle to find Boggle's top-scoring board",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/from?site=ft.com",
        "text": "ft.com",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/user?id=DavidSJ",
        "text": "DavidSJ",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44082892",
        "text": "3 hours ago",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/hide?id=44082892&goto=news",
        "text": "hide",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44082892",
        "text": "14 comments",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/vote?id=44081586&how=up&goto=news",
        "text": "",
        "type": "a"
      },
      {
        "href": "https://paulgraham.com/goodwriting.html",
        "text": "Good Writing",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/from?site=paulgraham.com",
        "text": "paulgraham.com",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/user?id=oli5679",
        "text": "oli5679",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44081586",
        "text": "7 hours ago",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/hide?id=44081586&goto=news",
        "text": "hide",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44081586",
        "text": "144 comments",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/vote?id=44083580&how=up&goto=news",
        "text": "",
        "type": "a"
      },
      {
        "href": "https://acoup.blog/2025/05/23/collections-the-logistics-of-road-war-in-the-wasteland/",
        "text": "The Logistics of Road War in the Wasteland",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/from?site=acoup.blog",
        "text": "acoup.blog",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/user?id=ecliptik",
        "text": "ecliptik",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44083580",
        "text": "2 hours ago",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/hide?id=44083580&goto=news",
        "text": "hide",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44083580",
        "text": "11 comments",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/vote?id=44080803&how=up&goto=news",
        "text": "",
        "type": "a"
      },
      {
        "href": "https://gitlab.com/sephalon/rotary_dial_kmod",
        "text": "Show HN: Rotary Phone Dial Linux Kernel Driver",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/from?site=gitlab.com/sephalon",
        "text": "gitlab.com/sephalon",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/user?id=sephalon",
        "text": "sephalon",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44080803",
        "text": "9 hours ago",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/hide?id=44080803&goto=news",
        "text": "hide",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44080803",
        "text": "35 comments",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/vote?id=44082968&how=up&goto=news",
        "text": "",
        "type": "a"
      },
      {
        "href": "https://blog.samaltman.com/please-fund-more-science",
        "text": "Please Fund More Science (2020)",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/from?site=samaltman.com",
        "text": "samaltman.com",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/user?id=ssuds",
        "text": "ssuds",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44082968",
        "text": "3 hours ago",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/hide?id=44082968&goto=news",
        "text": "hide",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/item?id=44082968",
        "text": "34 comments",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/vote?id=44080533&how=up&goto=news",
        "text": "",
        "type": "a"
      },
      {
        "href": "https://magnus919.com/2025/05/the-xenon-death-flash-how-a-camera-nearly-killed-the-raspberry-pi-2/",
        "text": "The Xenon Death Flash: How a Camera Nearly Killed the Raspberry Pi 2",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/from?site=magnus919.com",
        "text": "magnus919.com",
        "type": "a"
      },
      {
        "href": "https://news.ycombinator.com/user?id=DamonHD",
        "text": "DamonHD",
        "type": "a"
      },
      {
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        "text": "Why Algebraic Effects?",
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