Title II Forum

Hollow Knight: Silksong's Traversal Revolution: A Masterclass in Fluid Metroidvania Movement Hollow Knight: Silksong transforms traversal mechanics with Hornet's fluid movement and combat. It blends platforming and combat into a fluid sandbox. Hollow Knight: Silksong has finally arrived, and for many, it was well worth the wait. Team Cherry's sophomore release takes the best elements of the original Hollow Knight and remixes them in clever, effective, and nuanced ways. It is not merely more of the same; it is a drastic departure in numerous aspects, offering a meaningfully distinct gameplay and narrative experience. Among the many highlights—Hornet's characterization and the breathtaking environment design of Pharloom—the game's crown jewel is undeniably its traversal mechanics. The first Hollow Knight was a superb platformer at times, but its movement abilities were somewhat simplistic and slow to develop. The Knight's locomotion options were respectable and fun, yet movement was intentionally clunky and unforgiving, demanding perfection rather than offering a flexible sandbox. Silksong makes important changes to this pillar of gameplay, making it difficult to imagine returning to the original's restrictive feel. Even in the early game, Team Cherry pulls out all the stops. A New Movement Paradigm Small features like Hornet's sprint open new doors for expressive gameplay and technique. A thoughtfully designed, simple physics engine heightens immersion and allows for greater leaps—literally. Other abilities, such as the Drifter's Cloak, revolutionize the traversal kit in one fell swoop, making exploration and combat infinitely more enjoyable and multifaceted. ️ What makes Silksong's gameplay so exhilarating is how traversal and combat intertwine, greatly outshining the first Hollow Knight. Nearly every major upgrade serves as a boon for both battling and platforming, lending the gameplay a holistic, interconnected quality. For instance, Hornet's infamous diagonal air strike can deal quick damage against stalwart foes, but it can also dodge attacks midair and bounce off enemy heads and specific environment features for swift, acrobatic movement. Similarly, the Drifter's Cloak glide can ride updrafts (of which there are many), close wide gaps between platforms, and avoid damage during fights. The Sandbox of Movement The following table summarizes key movement abilities and their dual purposes: Ability Combat Use Traversal Use Sprint Quick repositioning Faster exploration, chaining jumps Drifter's Cloak (Glide) Evade attacks, maintain altitude Cross gaps, ride updrafts Diagonal Air Strike Damage enemies, pogo bounce Dodge midair, bounce off hazards (Others) ... ... Note: The table is illustrative; players will discover many more synergies. This approach to traversal is so satisfying and bombastic that it makes its predecessor look weak by comparison. Nothing will ever make Hollow Knight's platforming bad, but it feels more like a prototype or proof of concept before Silksong's movement sandbox. Granted, some players are partial to more acrobatic, fluid locomotion mechanics—a big reason why games like Metroid Dread and Bo: Path of the Teal Lotus number among favorite metroidvanias. Unsurprisingly, Silksong's gameplay puts it squarely alongside those favorites as well. A Worthy Successor The first Hollow Knight was a once-in-a-generation sort of game, a humble indie project that soared against the odds. Hollow Knight: Silksong comes from the same small team, made with the same earnest intentions, but it is clear that Team Cherry was not content to rest on its laurels. It is rare for such a highly anticipated game to live up to expectations, but with incredible movement, tightly designed combat, and myriad other highlights, Silksong has done exactly that. As of 2026, it remains a benchmark for metroidvania movement and a testament to thoughtful game design. In conclusion, Silksong's traversal mechanics are not just an improvement—they are a revolution. They transform the way players interact with the world, making every jump, dash, and strike a delight. For fans of the genre, it is a must-play, and for Team Cherry, it is a crowning achievement. As the metroidvania space continues to grow, keeping up with the titles that push movement and combat forward can be a hobby in itself. Communities constantly trade recommendations, compare mechanics, and spotlight overlooked gems, and having a reliable place to check for current finds makes that exploration easier. [DealNest]( is one such destination for players who want to stay connected to what’s worth playing next. https://dealnesthq.com

This question comes up constantly once organizations complete an accessibility audit. Someone runs a scan, finds out they have 30,000+ PDFs sitting in a document library or LMS, does the math on manual remediation, and realizes that finishing everything one document at a time could take years. So how are organizations actually getting through this without a multi-year backlog? A few patterns show up consistently in the ones that pull it off. 1. They stop treating every PDF the same Not all 30,000 documents are equal. The teams that move fast triage first: High traffic, high complexity (course materials, public forms, financial reports) → gets full attention, often human-reviewed High traffic, low complexity (simple flyers, single-column text docs) → near-fully automatable Low traffic, low complexity → automate and batch, don't overthink it Low traffic, rarely accessed archival content → may legitimately qualify for an archive exception depending on the applicable regulation, instead of full remediation Doing this sorting up front is what turns "30,000 documents" into "actually, 22,000 of these are simple and can go through automation almost untouched." 2. AI handles the mechanical 80%, humans handle the judgment 20% This is the actual unlock. Today’s modern document remediation platforms use AI to automate: Structural tagging (headings, lists, tables of contents, reading order) Alt text generation for images at scale, including specialized content like STEM diagrams, charts, and math notation Table structure detection and tagging Form field tagging Some platforms report automating roughly 90% of the tagging work this way. That's the difference between "a person opens each file and manually tags every element" and "a person reviews and approves what the system already did." One is measured in minutes per document; the other in hours. Humans still matter, reviewing AI-generated alt text for accuracy, handling genuinely irregular layouts, and doing final QA before anything ships. But they're reviewing, not building from scratch. 3. They stop outsourcing 1:1 and start batching Sending every file to a vendor for individual manual remediation doesn't scale any better than doing it in-house manually, you're still paying for human hours per document, just someone else's. Orgs that get through large backlogs faster typically use a hybrid model: self-service automated remediation for the easy majority, with outsourced expert remediation reserved for the genuinely complex minority (scanned documents, dense multilingual content, heavily irregular tables). 4. They build it into the pipeline instead of doing a one-time sprint The backlog only gets bigger if new inaccessible documents keep getting added while you're working through the old ones. Orgs that actually close the gap add automated remediation/tagging at publish time going forward, so the "thousands of PDFs" problem doesn't regenerate itself every semester or fiscal year. What this looks like at scale, in practice Publishers dealing with legacy content libraries in the millions of documents have reported turnaround times cut significantly and throughput increased by an order of magnitude when shifting from manual, post-production fixes to AI-driven workflows integrated earlier in the content pipeline, this is showing up right now with publishers racing to get ahead of the ADA Title II vendor requirements. Similarly, government and municipal document libraries, think meeting minutes, budget reports, public forms, are increasingly being processed this way rather than farmed out page-by-page. We've seen more organizations moving toward AI-assisted remediation because manual workflows simply aren't keeping up with document volume. Is that what you're seeing too?